{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfa6ce90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37521df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95caf822",
   "metadata": {},
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "937a286f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>goodsID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>chanelID</th>\n",
       "      <th>platfromType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sys-2018-254118088</td>\n",
       "      <td>user-157213</td>\n",
       "      <td>PR000064</td>\n",
       "      <td>272.51</td>\n",
       "      <td>272.51</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>APP</td>\n",
       "      <td>2018-02-14 12:20:36</td>\n",
       "      <td>2019-02-28 13:38:41</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sys-2018-263312190</td>\n",
       "      <td>user-191121</td>\n",
       "      <td>PR000583</td>\n",
       "      <td>337.93</td>\n",
       "      <td>337.93</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>Wech atMP</td>\n",
       "      <td>2018-08-14 09:40:34</td>\n",
       "      <td>2019-01-01 14:47:14</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sys-2018-188208169</td>\n",
       "      <td>user-211918</td>\n",
       "      <td>PR000082</td>\n",
       "      <td>905.68</td>\n",
       "      <td>891.23</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>We c hatMP</td>\n",
       "      <td>2018-11-02 20:17:25</td>\n",
       "      <td>2019-01-19 20:06:35</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sys-2018-203314910</td>\n",
       "      <td>user-201322</td>\n",
       "      <td>PR000302</td>\n",
       "      <td>786.27</td>\n",
       "      <td>688.88</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>WEB</td>\n",
       "      <td>2018-11-19 10:36:39</td>\n",
       "      <td>2019-08-07 12:24:35</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sys-2018-283989279</td>\n",
       "      <td>user-120872</td>\n",
       "      <td>PR000290</td>\n",
       "      <td>550.77</td>\n",
       "      <td>542.51</td>\n",
       "      <td>渠道-9527</td>\n",
       "      <td>APP</td>\n",
       "      <td>2018-12-26 11:19:16</td>\n",
       "      <td>2019-10-01 07:42:43</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               orderID       userID   goodsID  orderAmount  payment chanelID  \\\n",
       "id                                                                             \n",
       "1   sys-2018-254118088  user-157213  PR000064       272.51   272.51  渠道-0396   \n",
       "2   sys-2018-263312190  user-191121  PR000583       337.93   337.93  渠道-0765   \n",
       "3   sys-2018-188208169  user-211918  PR000082       905.68   891.23  渠道-0530   \n",
       "4   sys-2018-203314910  user-201322  PR000302       786.27   688.88  渠道-0530   \n",
       "5   sys-2018-283989279  user-120872  PR000290       550.77   542.51  渠道-9527   \n",
       "\n",
       "   platfromType           orderTime             payTime chargeback  \n",
       "id                                                                  \n",
       "1          APP  2018-02-14 12:20:36 2019-02-28 13:38:41          否  \n",
       "2     Wech atMP 2018-08-14 09:40:34 2019-01-01 14:47:14          是  \n",
       "3    We c hatMP 2018-11-02 20:17:25 2019-01-19 20:06:35          否  \n",
       "4          WEB  2018-11-19 10:36:39 2019-08-07 12:24:35          否  \n",
       "5          APP  2018-12-26 11:19:16 2019-10-01 07:42:43          否  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 提取2019年的订单数据\n",
    "df = pd.read_excel('某电商网站订单数据.xlsx', index_col='id')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "37c7a848",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 104557 entries, 1 to 104557\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   orderID       104557 non-null  object        \n",
      " 1   userID        104557 non-null  object        \n",
      " 2   goodsID       104557 non-null  object        \n",
      " 3   orderAmount   104557 non-null  float64       \n",
      " 4   payment       104557 non-null  float64       \n",
      " 5   chanelID      104549 non-null  object        \n",
      " 6   platfromType  104557 non-null  object        \n",
      " 7   orderTime     104557 non-null  datetime64[ns]\n",
      " 8   payTime       104557 non-null  datetime64[ns]\n",
      " 9   chargeback    104557 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(2), object(6)\n",
      "memory usage: 8.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "df.drop_duplicates('orderID', inplace=True)\n",
    "df.drop('goodsID', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4c296ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "start = datetime(2019, 1, 1)\n",
    "end = datetime(2019, 12, 31, 23, 59, 59)\n",
    "df.drop(df[df.orderTime < start].index, inplace=True)\n",
    "df.drop(df[df.orderTime > end].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9b7d86e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 处理业务流程不符的数据（支付时间早于下单时间、支付时长超过30分钟、订单金额小于0、支付金额小于0）\n",
    "df.drop(df[df.payTime < df.orderTime].index, inplace=True) # 支付时间早于下单时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5105b06f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 支付时长超过30分钟\n",
    "df.drop(df[(df.payTime - df.orderTime).dt.days > 0].index, inplace=True) # 超过一天\n",
    "df.drop(df[(df.payTime - df.orderTime).dt.seconds > 1800].index, inplace=True) # 超过30分钟"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8b2e96e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df.payment < 0].index, inplace=True) # 支付金额小于0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f2ee6909",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[df.orderAmount < 0].index, inplace=True) # 订单金额小于0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "165de345",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 处理渠道为空的数据（补充众数）\n",
    "df.rename(columns={'chanelID': 'channelID', 'platfromType': 'platformType'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a604ae31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.channelID.isna()]\n",
    "df.channelID.fillna(df.channelID.mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6887bb03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. 处理平台类型字段（去掉多余空格，保持数据一致）\n",
    "df.platformType = df.platformType.str.replace(r'\\s', '', regex=True).str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b17c27a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>channelID</th>\n",
       "      <th>platformType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sys-2019-279103297</td>\n",
       "      <td>user-146548</td>\n",
       "      <td>425.20</td>\n",
       "      <td>425.20</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:12:23</td>\n",
       "      <td>2019-01-01 00:13:37</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sys-2019-316686066</td>\n",
       "      <td>user-104210</td>\n",
       "      <td>1764.37</td>\n",
       "      <td>1707.04</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:23:06</td>\n",
       "      <td>2019-01-01 00:23:32</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sys-2019-306447069</td>\n",
       "      <td>user-104863</td>\n",
       "      <td>499.41</td>\n",
       "      <td>480.42</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 01:05:50</td>\n",
       "      <td>2019-01-01 01:06:17</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sys-2019-290267674</td>\n",
       "      <td>user-206155</td>\n",
       "      <td>1103.00</td>\n",
       "      <td>1050.95</td>\n",
       "      <td>渠道-0330</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 01:16:12</td>\n",
       "      <td>2019-01-01 01:16:25</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sys-2019-337079027</td>\n",
       "      <td>user-137939</td>\n",
       "      <td>465.41</td>\n",
       "      <td>465.41</td>\n",
       "      <td>渠道-9527</td>\n",
       "      <td>alimp</td>\n",
       "      <td>2019-01-01 01:31:00</td>\n",
       "      <td>2019-01-01 01:31:36</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sys-2019-417411381</td>\n",
       "      <td>user-181957</td>\n",
       "      <td>279.53</td>\n",
       "      <td>279.53</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 01:36:17</td>\n",
       "      <td>2019-01-01 01:36:56</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sys-2019-254286596</td>\n",
       "      <td>user-174586</td>\n",
       "      <td>622.70</td>\n",
       "      <td>622.70</td>\n",
       "      <td>渠道-0283</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 01:37:00</td>\n",
       "      <td>2019-01-01 01:37:14</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>sys-2019-303647260</td>\n",
       "      <td>user-178023</td>\n",
       "      <td>969.61</td>\n",
       "      <td>913.58</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 02:11:23</td>\n",
       "      <td>2019-01-01 02:12:56</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>sys-2019-347419495</td>\n",
       "      <td>user-209896</td>\n",
       "      <td>279.18</td>\n",
       "      <td>225.15</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 02:31:13</td>\n",
       "      <td>2019-01-01 02:32:40</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>sys-2019-384544993</td>\n",
       "      <td>user-148994</td>\n",
       "      <td>3424.78</td>\n",
       "      <td>3424.78</td>\n",
       "      <td>渠道-0530</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 02:57:16</td>\n",
       "      <td>2019-01-01 02:57:42</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               orderID       userID  orderAmount  payment channelID  \\\n",
       "id                                                                    \n",
       "6   sys-2019-279103297  user-146548       425.20   425.20   渠道-0765   \n",
       "7   sys-2019-316686066  user-104210      1764.37  1707.04   渠道-0396   \n",
       "8   sys-2019-306447069  user-104863       499.41   480.42   渠道-0007   \n",
       "9   sys-2019-290267674  user-206155      1103.00  1050.95   渠道-0330   \n",
       "10  sys-2019-337079027  user-137939       465.41   465.41   渠道-9527   \n",
       "11  sys-2019-417411381  user-181957       279.53   279.53   渠道-0007   \n",
       "12  sys-2019-254286596  user-174586       622.70   622.70   渠道-0283   \n",
       "13  sys-2019-303647260  user-178023       969.61   913.58   渠道-0765   \n",
       "14  sys-2019-347419495  user-209896       279.18   225.15   渠道-0396   \n",
       "15  sys-2019-384544993  user-148994      3424.78  3424.78   渠道-0530   \n",
       "\n",
       "   platformType           orderTime             payTime chargeback  \n",
       "id                                                                  \n",
       "6      wechatmp 2019-01-01 00:12:23 2019-01-01 00:13:37          否  \n",
       "7      wechatmp 2019-01-01 00:23:06 2019-01-01 00:23:32          否  \n",
       "8      wechatmp 2019-01-01 01:05:50 2019-01-01 01:06:17          否  \n",
       "9           app 2019-01-01 01:16:12 2019-01-01 01:16:25          否  \n",
       "10        alimp 2019-01-01 01:31:00 2019-01-01 01:31:36          否  \n",
       "11          app 2019-01-01 01:36:17 2019-01-01 01:36:56          否  \n",
       "12     wechatmp 2019-01-01 01:37:00 2019-01-01 01:37:14          否  \n",
       "13          app 2019-01-01 02:11:23 2019-01-01 02:12:56          否  \n",
       "14          app 2019-01-01 02:31:13 2019-01-01 02:32:40          否  \n",
       "15     wechatmp 2019-01-01 02:57:16 2019-01-01 02:57:42          否  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4cf5a2d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 添加折扣字段，处理折扣大于1的字段（将支付金额改为‘订单金额’ * ‘平均折扣’\n",
    "df['discount'] = np.round(df.payment / df.orderAmount, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "297645e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_discount = np.mean(df[df.discount <= 1].discount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "7d64dd23",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.discount = df.discount.apply(lambda x: mean_discount if x > 1 else x) # 折扣处理为正常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "d9a84585",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.payment = df.orderAmount * df.discount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "f6a73d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103321, 10)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39b7867",
   "metadata": {},
   "source": [
    "### 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "d11f1177",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 6. 交易总金额（GMV）、总销售额、实际销售额、退货率、客单价\n",
    "gmv = df.orderAmount.sum() # 交易总额\n",
    "total_payment = df.payment.sum() # 总销售额\n",
    "real_payment = df[df.chargeback == '否'].payment.sum() # 实际销售额\n",
    "back_rate = df[df.chargeback == '是'].orderID.count() / df.shape[0] # 退货率\n",
    "user_count = df.userID.nunique() # 用户数，不重复用户的数量\n",
    "arpu = real_payment / user_count # 客单价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "a0ef8fe0",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "6          1\n",
       "7          1\n",
       "8          1\n",
       "9          1\n",
       "10         1\n",
       "          ..\n",
       "104297    12\n",
       "104298    12\n",
       "104299    12\n",
       "104300    12\n",
       "104301    12\n",
       "Name: month, Length: 103321, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7. 每月GMV及趋势分析（折线图）\n",
    "df['month'] = df.orderTime.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "a6d89c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "gmv_by_month = df.groupby('month').orderAmount.sum() / 10000 # 每月交易额\n",
    "pay_by_month = df.groupby('month').payment.sum() / 10000 # 每月销售额\n",
    "real_by_month = df[df.chargeback == '否'].groupby('month').payment.sum() / 10000 # 没有实际销售额"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "f859cff5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4), dpi=120)\n",
    "gmv_by_month.plot(kind='line', label='每月总交易额')\n",
    "pay_by_month.plot(kind='line', label='每月总销售额')\n",
    "real_by_month.plot(kind='line', label='每月实际销售额', marker='*')\n",
    "plt.xlabel('月份')\n",
    "plt.xticks(ticks=list(range(1, 13)), labels=list(range(1, 13)))\n",
    "plt.ylabel('金额（万元）')\n",
    "plt.legend()\n",
    "plt.grid(axis='y', alpha=0.5, linestyle='--')\n",
    "for i in range(1, 13):\n",
    "    plt.text(i, real_by_month[i]-50, np.round(real_by_month[i], 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "6ff40473",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8. 流量渠道来源分析（饼图）\n",
    "channel_count = df.groupby('channelID').orderID.count()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "5365ff41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6), dpi=120)\n",
    "channel_count.plot(\n",
    "    kind='pie', \n",
    "    autopct='%.2f%%', # 百分比\n",
    "    pctdistance=0.8, # 数字距离中心的距离\n",
    "    wedgeprops={\n",
    "        'width': 0.35, # 挖空的大小\n",
    "        'linewidth': 1,\n",
    "        'edgecolor': 'white'\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "76cfffe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9. 周一到周日哪天的下单量最高、每天哪个时段下单量最高（柱状图）\n",
    "df['week'] = df.orderTime.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "61e15aa5",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week\n",
       "0    1757.029399\n",
       "1    1674.820046\n",
       "2    1472.414664\n",
       "3    1395.040908\n",
       "4    1393.015172\n",
       "5    1532.177407\n",
       "6    1625.095374\n",
       "Name: orderAmount, dtype: float64"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_by_week = df.groupby('week').orderAmount.sum() / 10000\n",
    "order_by_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "5d4712cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "order_by_week = order_by_week.reindex([1, 2, 3, 4, 5, 6, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "522e47b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(6, 4), dpi=120)\n",
    "order_by_week.plot(kind='bar', color=np.random.rand(7, 3))\n",
    "plt.xlabel('星期')\n",
    "plt.xticks(ticks=list(range(7)), labels='一二三四五六日', rotation=0)\n",
    "plt.ylabel('金额（万元）')\n",
    "for ind, val in enumerate(order_by_week):\n",
    "    plt.text(ind, val, np.round(val, 1), ha='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "f5591aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 统计哪个时段下单量最高\n",
    "df['time'] = df.orderTime.dt.floor('30T').dt.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "0e0cb06c",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_count = df.groupby('time').orderID.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "3107141d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4), dpi=120)\n",
    "time_count.plot(kind='bar', color=np.random.rand(48, 3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "c186478b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. 用户复购率\n",
    "df1 = pd.pivot_table(data = df, index='userID', columns='month', values='orderID', aggfunc='count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "7f84ffcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_data(x):\n",
    "    return 1 if x > 1 else 0 if x == 1 else np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "651a6bf2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "month         1   2    3    4    5   6   7   8   9    10   11   12\n",
       "userID                                                            \n",
       "user-100000  NaN NaN  NaN  NaN  NaN NaN NaN NaN NaN  0.0  NaN  NaN\n",
       "user-100003  NaN NaN  NaN  NaN  0.0 NaN NaN NaN NaN  NaN  NaN  NaN\n",
       "user-100006  NaN NaN  NaN  NaN  NaN NaN NaN NaN NaN  NaN  0.0  NaN\n",
       "user-100007  0.0 NaN  NaN  NaN  NaN NaN NaN NaN NaN  NaN  NaN  NaN\n",
       "user-100008  NaN NaN  NaN  NaN  NaN NaN NaN NaN NaN  NaN  0.0  NaN\n",
       "...          ...  ..  ...  ...  ...  ..  ..  ..  ..  ...  ...  ...\n",
       "user-299980  NaN NaN  NaN  NaN  NaN NaN NaN NaN NaN  0.0  NaN  NaN\n",
       "user-299983  NaN NaN  NaN  NaN  NaN NaN NaN NaN NaN  NaN  NaN  0.0\n",
       "user-299989  NaN NaN  NaN  0.0  NaN NaN NaN NaN NaN  NaN  0.0  NaN\n",
       "user-299992  0.0 NaN  NaN  NaN  NaN NaN NaN NaN NaN  NaN  NaN  NaN\n",
       "user-299995  NaN NaN  0.0  NaN  NaN NaN NaN NaN NaN  NaN  NaN  NaN\n",
       "\n",
       "[70592 rows x 12 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = df1.applymap(handle_data)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "010332b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     1.826630\n",
       "2     1.141227\n",
       "3     1.742581\n",
       "4     2.020669\n",
       "5     3.055556\n",
       "6     3.054025\n",
       "7     2.522445\n",
       "8     3.164489\n",
       "9     2.472377\n",
       "10    2.791820\n",
       "11    2.911985\n",
       "12    3.087313\n",
       "dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshoppint_rate = temp_df.sum() / temp_df.count() * 100\n",
    "reshoppint_rate # 复购率"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef75c55f",
   "metadata": {},
   "source": [
    "# 数据建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "eee8aca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(89710, 13)"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除退了货的用户\n",
    "df.drop(df[df.chargeback=='是'].index, inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "id": "e1db7ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderID</th>\n",
       "      <th>userID</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>payment</th>\n",
       "      <th>channelID</th>\n",
       "      <th>platformType</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>payTime</th>\n",
       "      <th>chargeback</th>\n",
       "      <th>discount</th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "      <th>time</th>\n",
       "      <th>F</th>\n",
       "      <th>R</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sys-2019-279103297</td>\n",
       "      <td>user-146548</td>\n",
       "      <td>425.20</td>\n",
       "      <td>425.200000</td>\n",
       "      <td>渠道-0765</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:12:23</td>\n",
       "      <td>2019-01-01 00:13:37</td>\n",
       "      <td>否</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>sys-2019-316686066</td>\n",
       "      <td>user-104210</td>\n",
       "      <td>1764.37</td>\n",
       "      <td>1707.027975</td>\n",
       "      <td>渠道-0396</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 00:23:06</td>\n",
       "      <td>2019-01-01 00:23:32</td>\n",
       "      <td>否</td>\n",
       "      <td>0.9675</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>sys-2019-306447069</td>\n",
       "      <td>user-104863</td>\n",
       "      <td>499.41</td>\n",
       "      <td>480.432420</td>\n",
       "      <td>渠道-0007</td>\n",
       "      <td>wechatmp</td>\n",
       "      <td>2019-01-01 01:05:50</td>\n",
       "      <td>2019-01-01 01:06:17</td>\n",
       "      <td>否</td>\n",
       "      <td>0.9620</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sys-2019-290267674</td>\n",
       "      <td>user-206155</td>\n",
       "      <td>1103.00</td>\n",
       "      <td>1050.938400</td>\n",
       "      <td>渠道-0330</td>\n",
       "      <td>app</td>\n",
       "      <td>2019-01-01 01:16:12</td>\n",
       "      <td>2019-01-01 01:16:25</td>\n",
       "      <td>否</td>\n",
       "      <td>0.9528</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sys-2019-337079027</td>\n",
       "      <td>user-137939</td>\n",
       "      <td>465.41</td>\n",
       "      <td>465.410000</td>\n",
       "      <td>渠道-9527</td>\n",
       "      <td>alimp</td>\n",
       "      <td>2019-01-01 01:31:00</td>\n",
       "      <td>2019-01-01 01:31:36</td>\n",
       "      <td>否</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>01:30:00</td>\n",
       "      <td>1</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               orderID       userID  orderAmount      payment channelID  \\\n",
       "id                                                                        \n",
       "6   sys-2019-279103297  user-146548       425.20   425.200000   渠道-0765   \n",
       "7   sys-2019-316686066  user-104210      1764.37  1707.027975   渠道-0396   \n",
       "8   sys-2019-306447069  user-104863       499.41   480.432420   渠道-0007   \n",
       "9   sys-2019-290267674  user-206155      1103.00  1050.938400   渠道-0330   \n",
       "10  sys-2019-337079027  user-137939       465.41   465.410000   渠道-9527   \n",
       "\n",
       "   platformType           orderTime             payTime chargeback  discount  \\\n",
       "id                                                                             \n",
       "6      wechatmp 2019-01-01 00:12:23 2019-01-01 00:13:37          否    1.0000   \n",
       "7      wechatmp 2019-01-01 00:23:06 2019-01-01 00:23:32          否    0.9675   \n",
       "8      wechatmp 2019-01-01 01:05:50 2019-01-01 01:06:17          否    0.9620   \n",
       "9           app 2019-01-01 01:16:12 2019-01-01 01:16:25          否    0.9528   \n",
       "10        alimp 2019-01-01 01:31:00 2019-01-01 01:31:36          否    1.0000   \n",
       "\n",
       "    month  week      time  F   R   M  \n",
       "id                                    \n",
       "6       1     1  00:00:00  1 NaT NaN  \n",
       "7       1     1  00:00:00  1 NaT NaN  \n",
       "8       1     1  01:00:00  1 NaT NaN  \n",
       "9       1     1  01:00:00  1 NaT NaN  \n",
       "10      1     1  01:30:00  1 NaT NaN  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 目的就是增加RFM列\n",
    "# 增加F列\n",
    "df['F'] = 1\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "70b406ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>orderAmount</th>\n",
       "      <th>orderTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>1</td>\n",
       "      <td>1978.47</td>\n",
       "      <td>2019-10-13 18:46:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>521.60</td>\n",
       "      <td>2019-05-24 13:04:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>1</td>\n",
       "      <td>466.89</td>\n",
       "      <td>2019-11-14 15:37:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>2178.20</td>\n",
       "      <td>2019-01-14 18:45:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>1</td>\n",
       "      <td>4949.65</td>\n",
       "      <td>2019-11-16 17:15:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "      <td>2019-10-18 10:53:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "      <td>2019-12-27 17:57:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "      <td>2019-11-11 10:40:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "      <td>2019-01-01 16:14:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "      <td>2019-03-30 16:35:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             F  orderAmount           orderTime\n",
       "userID                                         \n",
       "user-100000  1      1978.47 2019-10-13 18:46:46\n",
       "user-100003  1       521.60 2019-05-24 13:04:05\n",
       "user-100006  1       466.89 2019-11-14 15:37:19\n",
       "user-100007  1      2178.20 2019-01-14 18:45:35\n",
       "user-100008  1      4949.65 2019-11-16 17:15:03\n",
       "...         ..          ...                 ...\n",
       "user-299980  1       441.71 2019-10-18 10:53:37\n",
       "user-299983  1       706.80 2019-12-27 17:57:11\n",
       "user-299989  2      1685.18 2019-11-11 10:40:08\n",
       "user-299992  1       508.75 2019-01-01 16:14:47\n",
       "user-299995  1       479.94 2019-03-30 16:35:12\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df = pd.pivot_table(data=df, index='userID', values=['orderTime', 'F', 'orderAmount'], \n",
    "                        aggfunc={\n",
    "                            'orderTime': max,\n",
    "                            'F': sum, \n",
    "                            'orderAmount': sum\n",
    "                        }) \n",
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "id": "3e2eba92",
   "metadata": {},
   "outputs": [],
   "source": [
    "cur_date = datetime(2019, 12, 31, 23, 59, 59)\n",
    "res_df['R'] = (cur_date  - res_df.orderTime).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "fc9ac449",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df['M'] = res_df.orderAmount\n",
    "res_df.drop(columns=['orderAmount', 'orderTime'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "1497217e",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df = res_df.reindex(columns=['R', 'F', 'M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "f9e7676f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>221</td>\n",
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       "      <th>user-299980</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>441.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>706.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>1685.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>364</td>\n",
       "      <td>1</td>\n",
       "      <td>508.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>276</td>\n",
       "      <td>1</td>\n",
       "      <td>479.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               R  F        M\n",
       "userID                      \n",
       "user-100000   79  1  1978.47\n",
       "user-100003  221  1   521.60\n",
       "user-100006   47  1   466.89\n",
       "user-100007  351  1  2178.20\n",
       "user-100008   45  1  4949.65\n",
       "...          ... ..      ...\n",
       "user-299980   74  1   441.71\n",
       "user-299983    4  1   706.80\n",
       "user-299989   50  2  1685.18\n",
       "user-299992  364  1   508.75\n",
       "user-299995  276  1   479.94\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "20c77573",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R  F  M\n",
       "userID              \n",
       "user-100000  0  0  1\n",
       "user-100003  1  0  0\n",
       "user-100006  0  0  0\n",
       "user-100007  1  0  1\n",
       "user-100008  0  0  1\n",
       "...         .. .. ..\n",
       "user-299980  0  0  0\n",
       "user-299983  0  0  0\n",
       "user-299989  0  1  1\n",
       "user-299992  1  0  0\n",
       "user-299995  1  0  0\n",
       "\n",
       "[70592 rows x 3 columns]"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = res_df.apply(lambda x: x - x.mean()) # 处理每一列\n",
    "temp_df = temp_df.applymap(lambda x: '0' if x < 0 else '1') # 处理每个元素\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "98c36669",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags_dict = {\n",
    "        '111': '重要价值用户',\n",
    "        '101': '重要发展用户',\n",
    "        '011': '重要保持用户',\n",
    "        '001': '重要挽留用户',\n",
    "        '110': '一般价值用户',\n",
    "        '100': '一般发展用户',\n",
    "        '010': '一般保持用户',\n",
    "        '000': '一般挽留用户',\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "05257559",
   "metadata": {},
   "outputs": [],
   "source": [
    "def handle_df(x):\n",
    "    s1 = x[0] + x[1] + x[2]\n",
    "    return tags_dict[s1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "c96bf1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>TAG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user-100000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100003</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100006</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100007</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-100008</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>重要挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299980</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299983</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299989</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299992</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-299995</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70592 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R  F  M     TAG\n",
       "userID                      \n",
       "user-100000  0  0  1  重要挽留用户\n",
       "user-100003  1  0  0  一般发展用户\n",
       "user-100006  0  0  0  一般挽留用户\n",
       "user-100007  1  0  1  重要发展用户\n",
       "user-100008  0  0  1  重要挽留用户\n",
       "...         .. .. ..     ...\n",
       "user-299980  0  0  0  一般挽留用户\n",
       "user-299983  0  0  0  一般挽留用户\n",
       "user-299989  0  1  1  重要保持用户\n",
       "user-299992  1  0  0  一般发展用户\n",
       "user-299995  1  0  0  一般发展用户\n",
       "\n",
       "[70592 rows x 4 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df['TAG'] = temp_df.apply(handle_df,axis=1)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "5270f4f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df2 = temp_df.groupby('TAG').TAG.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "d88ce71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TAG\n",
       "一般价值用户     1373\n",
       "一般保持用户     3836\n",
       "一般发展用户    22311\n",
       "一般挽留用户    19813\n",
       "重要价值用户     2684\n",
       "重要保持用户     8062\n",
       "重要发展用户     6901\n",
       "重要挽留用户     5612\n",
       "Name: TAG, dtype: int64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "c5dd7376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x3KDj2K3bbgC46i5zxHkSeBL4OcDyjDFtiIVcY0wsuwG/cdU91REnpbiqWP/38//k1fmvMjd/btC1mQ6ib6e+HDPoGI4ZdEz0VsOfAI8AL2KT1Ywx9bCQa4yJSAEmqupvRGRPgB/W/cAL817gncXvUBmuDLg801E54rBXj704dtCxHNz3YE0KJYmr7gpHnLvwAq8tsmyM2YqFXGNMZ+AyV92LHXFyK2oq9I2f35AX573I7Hyb6G7iS2ZSJhOGTOC04adp59TO4qpb4ojzMHA3sDTo+owx8cNCrjEdV2/gKlfdCx1xUpcVL9Nn5zwrry98naKqoqBrM6ZeiU4i4weM56wRZ+mgnEGiqmEReQH4JzAj6PqMMcGzkGtMxzMIuFpVzxSRxHn583jkh0d4b+l7uGoT2E3bs3fPvTlrxFn8oucvIoc+Au4A3sZWZTCmw7KQa0zHsTNwjapOFBHn27Xf8sgPj/DZis+CrsuYZrFjzo6cMeIMxg8YT4KTENlG+J/AM4CtcWdMB2Mh15j27xfAtcAvAaasmMKjMx9l+prpwVZlTAvpltaNU4adwsQhEzUjKUP8LYT/gjdJrSro+owxrcNCrjHtkwCHquo1InKgqy7vL3mfx2Y+ZpPJTIeRnpjOCYNP4OyRZ0cmqS12xLkBeA4IB12fMaZlWcg1pv3ZW1XvEJFfhN0w//v5fzz242MsKlwUdF3GBCI1IZVThp7CuTudq52SOomq/igi1wGTAfshaEw7ZSHXmPZjEHArcGKNW8Mr81/h8ZmPs7J0ZdB1GRMXMpMyOXvk2Zw27DRNSUgRVf1CRK7B22DCGNPOWMg1pu3LA65X1UtEJOGDpR9w1/S7WFy0OOi6jIlLnVM7c+HOF3LikBNJcBIA3sHrW7elx4xpRyzkGtN2JQOXuOre4IiT9eP6H7njmztsQpkxjdS7U28u3uVixu8wHkccgJeAG4B5wVZmjGkOFnKNaXsEmOiqe5sjTv+VJSv1rhl3yduL3katvdCYJhuSM4RLR13KgX0OxN9U4jHgOmB90LUZY7adhVxj2pZ9VPWfIrJnSVWJPvTDQ/LsnGepcm1VJGO2165dduXy0ZczuttoXHU3OuL8AXgc21DCmDbJQq4xbcMA4HbghBq3hufnPs9DPzxEQWVBwGUZ0/4c3v9wrt7jau2c2jkyOe0i4Pug6zLGNI2FXGPiWwLwW1fdvzjipL635D3umn4XS4uXBl2XMe1aRmIGF+96MacMOwVBXBG5B/gzUBR0bcaYxrGQa0z8GqWqj4jI6KVFS/WmL26Sr1Z/FXRNxnQoQ3OHcsNeN7Bzl51x1V3liHM53gQ1++FpTJyzkGtM/EkF/qyqv3PVDT0x6wke/P5BKsOVQddlTIckCCcMPoErRl+hmcmZArwLXALMD7g0Y0w9LOQaE18OctV9xBFnhx/X/8iNn9/IvI22mpEx8SAnOYcrx1zJsYOORVWrReRWvA1YKoKuzRizNQu5xsSHXOAO4OyKmgq959t75Nk5zxLWcNB1GWNq2a3rbly/1/U6OGewuOr+7IhzAfBB0HUZY7ZkIdeYYAkwwVX3PkecLlNWTOGWabewomRF0HUZY+qRIAmcMuwULhl1iaYmpApwH3A1UBZwacYYn4VcY4LTB7gf+GVBZYH+/au/y/9+/l/QNRljmqB3Rm9u2feWyNq6CxxxTgemBV2XMcZCrjFBmeCq+6gjTubkhZO5/evb2Vi5MeiajDHbwBGH04adxm93+60mOokqIn8HbgRslxZjAuQEXYAxHUwa8AjwYkFlQaffvP8brp1yrQXcdqpkdgkrHl/BsvuXseGDDWh4y0GF6o3VzL1sLlXrtj0LVRdUM/s3s1n/VuwdaCuWVTDrgllbHV/1zCrmXDyHwq8KNx3b+OlG3Brb3KupXHV5evbTTPzfRJmTP8cBrlHVr4ARQddmTEdmIdeY1rOzq+504LzPV37O8a8dL5+t+CzomkwLKZhWwJL/WwICCdkJrHlpDcsfXb7p8XB5mKX3LqWmqGa7Xmf1s6tJyEwg79C8rR6r3ljN0nuXolVbh+uCzwvoNrEba15dA4Bb41K+tBwnwX4sbKuFBQs59Y1Tuf+7+3HV3cX/+/4bvN57Y0wrs3/NjGl5AvzGVfdrV92h//zmn/z6vV+zoWJD0HWZFuJWuax6ZhU9T+9Jr7N70eOUHvQ4vQeF0woJl4epKa5h0d8Wbfd2AsUziyn8qpAep/VAErbMUZVrK/n5lp8JpYe2el71+mqSeySTvU821eurASj8spCsPbO2ryBDjdbwwPcPcObbZ7KqZFUS8C/gNaBLwKUZ0+FYyDWmZeUCrwD/WlmyMvH0t07nyVlPorZZUrum1Uq347uRvW/2pmOJuYmgoGGlckUl6UPT6X1h721+DbfKZeXTK+m0Wyc6jey01ePlC8rJHZtLt4ndtq5PFRwQESLzMsrml5E+OH2b6zFb+n7d95ww+QSZvHAywFGuujOBcQGXZUyHYiHXmJazn6vuD8Bxb/78JhMmT5Af1/8YdE2mFYTSQ+SOzUUcb3TVrXbZ8O4G0oakkZCRQNqQNHqc2gMJbfun2Osmr6N6XTUiwrKHlrH+rfW41Zv7abP2yqLLL2MPHiZkJ1C1voqK5RUkZiVSOr+UtMFp21yLia20upRrp1zLHz/9I+U15V2Bd4C/AwkBl2ZMh2Ah15jmF8LblvfjqnBVzxum3sDVn11NSXVJ0HWZAKx9bS3zr55P5cpK+lzUB2BT+N1WNYU1rH/Hm2hWvbGa6vXVrH5xNYtuW7Qp6Nb3Gsldk0nunszCPy8kZ2yOtSq0sDcWvcEJr58gP6z7AeAPwFvA1k3UxphmZSHXmObVW1U/BG78aeNPMnHyRJm0YFLQNZkApfZPJW1IGtXrqyn4oqBZrlnweQFapXQ/uTsD/zSQHa7bgb6/7Uv5wnIKpjbuNfr/rj9D/jmEnH1ySMhMYMO7G+pdpcFsnxUlKzjr7bN4cd6LAIf4k9J2DbYqY9o3C7nGNJ+9XXVniMj+z8x5hlPeOEUWFS0KuiYTsE67dKLPr/t4Kxm8tIaqDdu/dGrlmkqcFIfcg3I3HcvcNZOEnAQqllY06hriCEl5SeR/kk/2Ptmse20dvc/rzdrX1m611JlpHtVuNX+Z9hdu/PxGwm64r6vuF8ApQddlTHtlIdeY5nGW357Q+YqPruC2r26jyrV14DsqrVGq1m/53z9zt0xQqFq9/f9fOMkOCdkJOIlb/hPuJDlN6vN1q1zcMhdxBCfNIXO3TJwUZ7uXNTP1e2X+K5z19lmyoXxDMvAMcCfWp2tMs7OQa8z2CQG3A0+sKVuTcOqbp8r7S98PuiYTsLKFZcz/4/wtRm0r11QCkNg5cbuvnzogler8asKl4U3HqtZVUbW+itSBqY2+TsHnBWTvne19YYO3reqH9T8wYfIEmbFmBsAVqvoutsyYMc3KQq4x2y4Tb/3L33279ltO+t9J8tPGn4KuycSBtMFppPRJYcmdSyiaXkTRjCJWPrWSTrt2IrlbcoPPD5eHKV9UjlsVe/exzNGZJOYksuSeJRT/UEzR9CKW3L2EpM5JZI7ObHSd5YvLSR2QSkKnBNxyl8KvC3ErXBIybVCxNWyo2MC5757Lc3OfQ0TG+n26uwVdlzHthYVcY7bNDq6604AjX1vwGue+c65t7mA2EUfo+9u+pPROYcXjK1j59EoyR2XS+9eNWxe3fHE5C29aSOWqypiPO4kOA64ZQFJeEssfXc7yh5eTkJVAvyv7bdXCUOdrLCknY3iGV2+C0OXoLqx4fAVdj+m6XUubmaapcWv425d/4/op11Pj1vR21f0cOCPouoxpDySyELgxptEOdNV9Bci9c/qdPDXrqaDrMca0A8PzhnPP2Hu0W3o3Ae4FrgSsQdqYbWQh15imuUBV/1VWUxb6/Se/l89WfBZ0PcaYdiQ3JZc7DriD3bvvDvA/4FdAWbBVGdM2Wcg1pnES8GZAX7qieIVe/OHFsrBgYdA1GWPaoQRJ4Ma9b+SYQcegql+IyC+B/KDrMqatsZBrTMM6AS8D475e/TVXfnwlBZUFAZdkjGnvLt/tcs7d6Vxcdec64owDlgVdkzFtiYVcY+qXp6pvicjuL//0Mn/98q/UuNYiZ4xpHacNO42r97gaV90VjjiHAbOCrsmYtsJCrjF16+Gq+54jzoiHvn+I+767L+h6jDEd0PgB47ll31s0JKFCR5wjgc+DrsmYtsBCrjGxDXDV/cARZ8Ad39xhKygYYwL1i56/4O6xd2tyKLnKEWci8HrQNRkT7yzkGrO1Ya667wM9b/7iZl6Z/0rQ9RhjDMPzhvPgIQ9qdnK2isgFwGNB12RMPLOQa8yWRrvqvuuqm3vNZ9fw9uK3g67HGGM26dupLw8f+rD26tRLgOuBv2GbMhsTk4VcYzbbz1X3jWq3OuOKj66wNXCNMXEpLyWPhw59SHfM3VGA+4DfArH3gDamA7OQa4znCFfd/1bUVCRd/MHF8s2ab4Kuxxhj6pSRmMHdB93NHt33AHgUuBALusZswUKuMTBBVZ8tqioKXfjehTJrg63QY4yJf0lOEncfdDf79toX4H7gEqx1wZhNnKALMCZg56jq8+vL14fOfPtMC7jGmDajyq3i8o8u58tVXwL8Bvg/QIKtypj4YSO5piM7Dfj3iuIVet6758nykuVB12OMMU2WmpDK/Qffz5juYwBuB67GRnSNsZBrOqxjVfXlNWVrnNPfOl1Wl64Ouh4TxxIkgZyUHLKSs8hOziYzOZPs5Gyykrb8OiMpgwRJQEQISWjTn4443g0HEaHaraa0unSrW1l1GSXVJZRWl1JcVcy68nWsKVvD+rL11KjttGfqlpaQxoOHPsiorqPAW3Hheizomg7OQq7piA5R1Tc3Vm5MOOOtM2RJ0ZKg6zFxICMxgz6d+tC7U2/vz4zeka+1R3oPCTmhep+vqq6ixUANEMabBLTpT0HCUV+nKtpJkE4ikthQba66FFQW6MqSlbKyZCUrSlYQ+XNR4SJWlKxALc90eBmJGTx86MPs1GUngD8DNwdckjGBspBrOppfuOq+X1Zdlnrm22fKTxt/Croe08oSnAQGZw9meN5whuUNY2juUPpn9tes5KytehlddcsEWSAiC4EVwAYgv44/C9m22e3JQCf/lhl1PxfoCfSK/Omq20+QniKyRa1l1WU6f+N8+WnjT8wvmM9PG39i/sb5FFUVbUM5pi3LTMrkkXGPMDxvOMC1wK0Bl2RMYCzkmo5kF1fdT6rCVZnnvXuefL/u+6DrMS0s0UlkcM5gRuSN8EJt7jCG5AzRxFDippDoqrveEWcO8DOwsNaf64i/j3yTgD5Af2AHYASwk6vuro44udEnrildo/M2zpPZG2Yzfc10vl/3PeU15a1fsWlVWclZPD7ucR2SO0SA3wH/DLomY4JgIdd0FENcdaeE3XDniz+4WL5Y9UXQ9ZgWkCAJjOw8kr167sVePfZi5y47k+hs7gZw1V3riPMNMD3qtoL4C7LbQoDuwE7AzsDOqroTMFxEkgBq3BpmrZ/F9DXT+WbNN3y79ltKqksCLNm0lJzkHJ44/AkdmD1Q8DaLuCfomoxpbRZyTUfQ11V3qqr2vvKTK/lw6YdB12Oa0eDswezVcy/27L4nu3ffXdMS0wTAVbfUEedT4Cs2B9qVQdYakERgV+AAYH9X3f0dcbIAwhrmp/yf+GbNN3y1+iumrZxGRbgiyFpNM8pLyePJw5/U/ln9BW+ziIeDrsmY1mQh17R33Vx1P3PEGXztZ9cy+efJQddjtlNaQhoH9DmAA3sfyJ499tS81DwBUNUaEfkC+AB4Hy/cVgdZa5xy8EZ798cLvQc44nQBqAxX6ucrPpcPl33IJ8s+YWPlxkALNduvS2oXnj7iae2V0UtF5JfAW0HXZExrsZBr2rNsVf1YRHb525d/47m5zwVdj9lG6YnpHNj7QMb1H8e+vfbVpFBSJNh+LyKRUPsZYJ+9N50AOwKH4S2tt5+IhFx1+Xbtt3y07CM+WvoRS4uXBlym2Vb9MvvxzPhntFNSpzJHnH0Am5BgOgQLuaa9SlPV90Rk73tm3MMjMx8Juh7TRBmJGRzY50DG9RvHPr320aRQkqiqKyKfAC8B/wVsgePmlweMB4511T3cEScNYGHBQn1/yfvy+sLXLfC2Qbt13Y1Hxz2qISe0yhFnT8B2vzHtnoVc0x45eCHo+CdnPck/v7GJxW1FopPIQX0O4siBR7Jvz301MZQYCbYfsTnYrg24zI4kBTgYL/AeE2lrmLFmBv9d8F/eXfwuZTVlwVZoGm38gPH8ff+/Rz4B2Q8oDromY1qShVzTHv0NuOadxe/w+09+b4vktwEDswdy/KDjOXrQ0ZqdnC2qGq4VbNcFXKKBEDAWONtV9wRHnOTymnJ9d/G7MmnBJL5Z803Q9ZlGuGDnC7h01KUAbwLH4G1eYky7ZCHXtDdnAU/MXDeTs985m8pwZdD1mDokOUmM6z+OiTtOjGxFiqrOE5FHgX8DawIt0NQnG/iVqp4tInsCLC9erpMWTJJJCyaxpsz+08Wzv+zzF44ddCzA/cAltI8l9IzZioVc054coKrvry5bHTr5fyfLhooNQddjYuiV0YsJQyZw/ODjNSclR1x1Kx1xXgAeAaZiP3DbmuHAWa66ZzridK1xa3h70ds8Nfsp5ubPDbo2E0OCk8CDhzzInj32BLgKuDPgkoxpERZyTXtxiKvuaxU1Famnv3W6bdcbh4bmDuW8nc7j0H6H4oiDq+5CR5z7gSfxtsY1bVsCcATexgMHA0xbNY2nZj3FlBVTAi3MbK1TYif+Pf7fukPWDojICXhtQca0KxZyTXuQqa67QhwnY3Xpao6adBQVNbagfbwY1XUU5+90Pvv13i9y6C3gLrxlv9yg6jItahRwlar+SkQSFhYs1CdnPSlv/PwG1a4tXRwveqb35LlfPqfZydmVjjgH4K0tbUy7YSHXtHUOMAk4qnLxYpL796e4soiT3ziFJcVLAi6tY9un5z6cv/P5jO42GlVVEXkJuBX4LuDSTOvpA1zmqvtrR5yMDeUb9Jk5z8hzc5+z7YTjxMjOI3nisCc0KZS03hFnD2Bx0DUZ01ws5Jq27hbguoJJr7Hquuvo9oc/kHvmGdSEq7nqk9/x4TLbwrc1OeJwcN+DOX+n8xmWNyyyC9nTwN8B6yHpuDKB81x1r3DE6V1UWaSPzHxEnpv7nE0OjQMH9T2I/zvw/xBkuojsA9h/FNMuWMg1bdlE4IXymTNZctrpaKX373L2hAl0//OfwHF48IeHuP/7+4OtsoPYr9d+XDXmKh2YPVBcdSsccR4G/gnYzgEmIhE41VX3JkecvmvL1uoD3z0gkxZMokZtJasgXbLrJVy4y4XgrbhwccDlGNMsLOSatmpndd1p4fz8lEXHnyA1a7fcHyBt993pfe89OFlZfLr8Uy758JKAymz/huYO5aoxV7FXj73ww+1dwP9hmzaYuiUDF7rq3uCI03lp0VK999t75Z3F79i61gFxxOHhQx+OrLhwMvB8wCUZs90s5Jq2KENddzquO2TJ6adT/u13MU9K7N2b3g/cT8rgwSwtWsqvJv+KkhrrA2wu3dK6cemoSzlq4FEIoiLyBPAnYEXQtZk2oxNwuavu7x1xOs3Nn8vdM+621RgCkpeSx8tHv6y5KbnljjijAVsDzrRpFnJNW/QUcMaav/+d/CeerPdEJz2dnnfcTqexYymtKuH0t85gfsH8VimyvUpPTOeckedw5ogzNTmULMB7wO+B7wMuzbRdecAfVfUyEUn6ctWX/HXaX1lUtCjoujqcMd3G8NhhjwHM8iei2b7Nps2ykGvamjOBJ4s//pjlF/0GGvP/r+PQ5Yor6Hz+eYTD1fxxyrW8vfjtFi+0vXHE4YTBJ3DJqEs0NyVXVHWWiPwOeAfbwME0j97ATcA51W61PvHjE/LID49QEbYlAVvTuSPP5fLRl4M3oHA29vfbtFEWck1bMlRdd0bNunUpi445VsIFBU16ctYxx9Djlr9AKMQTs57k/2b8X8tU2Q4Nzh7MjXvfyM5ddsZVd40jzvV4mzjYbCHTEvZS1QdFZJeVJSv1b1/+TT5Z/knQNXUYgnDfwfexf+/9Ac4FHg+4JGO2iYVc01akquo0XN15yZlnUv7NN9t2kV13pff9/yIhN5cvVn7BBe9d0Mxlti9JThIX7HwB5+50LiEJuSJyN17frTU3m5aWAPzGVfevjjgZHy39iFu/upVVpauCrqtDyErO4qWjXtJuad2qHHH2xNqRTBtkIde0FQ8Av1539z2sf+CB7bpQQo8e9HngflKGDmVlyUomTJ5AUVVR81TZjozuNpobf3Gj9s/qL6r6vYicD3wddF2mw+mJtxTdSRU1Ffrg9w/KU7Ofosa1DxFa2k6dd+KpI57SkIQW+hPR7B9K06ZYyDVtwQTgxdIvprH03HPB3f6dYCUtjZ633UbmuEMpqy7j7LfPZnb+7O2vtB3olNiJK0ZfwYQdJ+CqW+WI82e8kGH7sZogHeqqe78jzqD5G+frtVOulbn5Nvm/pZ067FT+uMcfAV4CfoX155o2xEKuiXc7qOt+Fy4oyFh0zLFSs25d811ZhM6XXEKXi39DOFzDDZ//ick/T26+67dBB/U9iOv3vF67pHURVf1YRC4AbDkKEy+S8VZhuD6s4YQHv3+QR2c+SljDQdfVrv3zgH8yrv84gEuAfwVcjjGNZiHXxLMkVZ0iIrsvPe88SqdMbZEXyRw/nh63/g1JTOS5ec9z61e3tsjrxLPUhFSu3v1qThhyAq66hY44V+FNNrF/IEw8GuWq+29HnBE/rv+Raz+71pYba0EZiRm8fNTL2iOjR6Ujzk7AgqBrMqYxnKALMKYeN4nI7usffrjFAi5A0ZtvsuTU0whv2MApw07hycOfxOlAfzWG5AzhhV++oCcMOQHgPUecYcBjWMA18etbR5wxwO0j8kboi0e9qCcOPjHomtqtkuoSrp96vTjipKjq41h2MG2EjeSaeDVGVadVzJoVWnzSyVDT8pNMErp2pfe//kXqTiNZU7qGCZMnsLFyY4u/bpBOHnoyvxvzO010EsMici1e7+32Nz0b03r2c9V9xhGnz/tL3ufGL26ksLIw6JrapWv3vJaTh54M8FvgnoDLMaZBFnJNPEpS151BODx80QknSOVPrdcSKsnJ9PjbX8k68kgqqss5591zmbl+Zqu9fmvJSs7i5r1v5qC+B+Gqu8gR5yTgq6DrMmYbZQMPAr9aW7ZWr/70avlmzbYtM2jqlpqQyn+P/q+1LZg2wz5yMPHoWnGcEesffLBVAy6AVlay8qrfsfauu0lJTOU/h/+b9vYx6Ohuo3nlqFf0oL4HATzniDMKC7imbSsATgbO7JzauezRcY9y6rBTAy6p/SmvKbe2BdOm2EiuiTe7qOo3lT/9FFp04gShOrhVqzodeig9//EPJDmJVxa8yk1f3BRYLc3l/J3O55JRlwCUO+JcjLdrmf0jYNqTYa66kxxxhkxeOJmbv7jZtgVuZta2YNoKC7kmniSq6pe47qjFEydSMSv4dWuThw6lz4MPkNi9Oz+s+4Ez3z6zTS5CnxxK5uZ9bmb8gPGo6kwRmQjYIqOmvcoCngaOnrNhDpd/dDkrS1cGXVO7YW0Lpq2wjxpMPPmdiIza8OijcRFwASrnzmXRCSdS9u237NxlZ9478T26pHYJuqwm6ZrWlScPf5LxA8YD/FdE9sYCrmnfCoHjgD8PyxvGC0e9oHt23zPomtoNa1swbYX9j2nixXBVvaly4UJd/6/7g65lC+ENG1h6xpkUTJpE59TOvHX8m4zpNiboshplRN4Inj/yeR3ZeSTAX4ATgZJgqzKmVbjAzcDRmUmZJQ+Pe5gzhp8RdE3txjdrvuG5uc8hIvvhbRJhTNyxdgUTD0KqOhXVPReffAoV338fdD11yj3nHLr+7ipUlX98czvPzH0m6JLqdHj/w7ll31s00UmscsQ5C3g+6JqMCcgQV93XHHGGvvnzm9ww9Qaq3Kqga2rzrG3BxDsbyTXx4Lcismf+U0/FdcAFyH/8cZb/5mKorOTqPa7mr/v8NeiStiIIl+x6CbcfcDuJTuIaR5z9sIBrOrafHHH2ACaN32E8Dxz6ABmJGUHX1OZZ24KJdzaSa4LWX113TvWyZck/H3OsaEXbmAWdPHgwvR+4n6TevZm7YS6nvnlqXIwMJTqJ3LbfbYzrPw5V/UZEjgVWBF2XMXHCAe4GLpmXP09//f6vZX35+qBravOu2/M6Thp6EsCZeBP+jIkLFnJN0F4GTlhy1tmUTZsWdC1NEsrOpte995C+++5srNjISZNPYmVZcDO4UxNSuXvs3fyi5y8AXgLOAsoCK8iY+CTANcBfV5as1Aveu0CWFC0JuqY2LTMpkzeOf0MzkzLXOuIMBoqDrskYsI8WTLDGAicUvftemwu4AOGCApaecy4bX3yJnJQc/nfc5EjAbHWZSZk8fOjDkdd/EDgJC7jGxKLA34DzeqT30P8c8R8dkTci6JratKKqIu6ZcY844nQDrg26HmMibCTXBCVBVb/V6uoRPx8xXqpXtO1P1HNOP41u11yDotw1426emPVEq712XkoeDx/6sA7JHSLA3/FGqewvtjENO8pV96XKcGXS5R9dLp+v/DzoetosRxxe/OWLDMkZUi0iw7FJaCYO2EiuCcoFIjIy/7HH23zABdj47/+w7IIL0NJyrhh9Bbfvf3urvG739O48dcRTkYB7DfBHLOAa01iTHXEOSg4lF/7r4H9x5IAjg66nzXLV5davbkVEEoE7gq7HGLCRXBOMXHXdBTXr1mUvPPwI0fLyoOtpNkkDBtDnwQdJ6teXBQULOHnyyVS4LTOZrn9mfx4Z94h2T+8uwMVAfC0wbEzbMdxV912g1w1Tb+D1ha8HXU+bdfv+t3P4gMMBxgHvBVyO6eBsJNcE4WZxnJy1t9/ergIuQNWiRSyaOJHSz79gUPYgPpj4AX069Wn219kxZ0eePuJp7ZbWzQVOxwKuMdtjtiPOPsCyv+zzl8jugGYb3Dn9TirDlaqqdwOJQddjOjYLuaa17aSqF5XNmEHR/94IupYW4RYWsvSCC8j/z3/ITM7k9WNe44DeBzTb9QdmD+Sxwx7T7OTsGhE5AfhPs13cmI5riSPOgcDKv+37N8b1Gxd0PW3SqtJVPDbzMRGRYcBFQddjOjZrVzCtSYAP1HXHLp4wgYpZs4Oup8Vl/+pXdL/henAc/vX9/Tz0w0Pbdb3eGb15+ointXNq57CIHA281TyVGmN8g111P3XV7X7Vx1fx4bIPg66nzUkJpTD5uMnaNa1rkb+k2LqgazIdk43kmtZ0HDC28JVXO0TABSh44QWWnnsubkkJF+96MfeMvWebr9U1rSuPHfaYdk7trCJyChZwjWkJ8x1xDnLEWXfHgXfofr32C7qeNqciXMEd39whjjhZwM1B12M6LhvJNa0lRV13rltW1nfhuMMknJ8fdD2tKrFvX/o8cD/JAweypHAJE/83kbKaxi9jm52czVOHP6U7ZO8gwLnA4y1WrDEGYKSr7idhN5xzyYeX2PJi2+DJw59kt667uSKyGxDfe7abdslGck1rOV8cp9/6+x/ocAEXoHrpUhb/6iRKPvmUfln9+GDC++yQtUOjnpuRmMGDhz4YCbhXYAHXmNbwoyPOwSEnVHjPQffo7t13D7qeNue2r25DUUdV78JrVzOmVVnINa0hTV33upr163Xjs88GXUtg3JISll10ERsef5yMpE68ctTLHNrv0HqfkxJK4b6D72NE3ggBbgLuao1ajTEAfOeIc0iik1hyz0H36MDsgUHX06bMzZ/LpAWTEJEDgYMDLsd0QBZyTWu4SByn2/oHHxStaJk1Y9sM12XtP25n5bXXEnLhn/vfwWWjLot5aoKTwJ0H3snobqPBC7c3tWKlxhjPdEec49IT0sMPHPKA5qXkBV1Pm/Lg9w9S7Varqv4FG801rcxCrmlpGeq6f6xevVoLXnwp6FriRuGr/2XJmWcRLijk/J3P58FDHtzqnBv2uoH9eu8HXnvCldhOZsYE5QMRuaBHeg+57+D7SE1IDbqeNmNV6Spe+ekVEZG9AFuA2LQqC7mmpV0ijtN5/QMPilZVBV1LXCmfMYPFE06k4qef2KfXPrx9/Nt0SuwEwNkjzub4wceDt4LChVjANSZoTwB/Hdl5JLftdxuO2I/Pxnpk5iNUhatsNNe0OvtbalpSprruH6pXrtSCV18Nupa4VL1iJUtOPpniDz6gV6devH/ie5w94mwuH305rrqzgJOAmqDrNMYAcAPw3EF9D+Kq0VcFXUubsbZsLS/Me0FEZBTwq6DrMR2HhVzTkn4rjpOz7l/3C9XVQdcSt9zSMpZfcinrH3qItKR0rtjtcoCNjji/BIqCrc4YE0WBs1V1yhkjzuCkHU8Kup424+NlHwOgqk8AoSBrMR2HrZNrWkqOuu7i6uXLOy0cf6RQY4ORDQnl5bHD/yYTyspCHMfWwjUmfuW56k4DBl3ywSV8tuKzoOuJWwOzB/LrnX/NuP7jols8zgKeCq4q01HYSK5pKVeK42Su/9f9FnAbIzGR3nffTUJODuI4v8YCrjHxbIMjzhFA/j8O+If27tQ76HrizsDsgdy+/+28evSrHD7gcJzSDfDW1VBdrqhegfXmmlZgI7mmJeSp6y6pWrIk7ecjfym4btD1xL3uN/6ZnJNOAvg/vJUUjDHx71BVfWdu/lw57c3TqHJtcu1WI7cl6+D9P8F3/hrph/0VfnEJwDHA6wGWajoAG8k1LeFKcZz09ffeZwG3ETKPOioScN8H/hBwOcaYxntPRG4eljeMq/e4OuhaAhVz5HbSRXDHoM0BF2DqPVBTqaj+CRvNNS3MRnJNc0tX111evWxZ1sIjxlvIbUBS//4M+O+rKsnJq8VxdgHWBV2TMaZJQsDbwCF//PSPvLHojaDraVUNjtzGMv522OMC8HZB+7CVSjUdUELQBZh25wxxnOz8p57GAm79JCmJXv93p0pKiorIKVjANaYtCgOnuup+d+PeN3afkz9Hfi78OeiaWtw2hduIL+6H3c8Dca7CQq5pQTaSa5qTo6471y0pGTT/wLGiZWVB1xPXut1wPbmnngredr03BluNMWY77auqH/9c+LNz8hsnS3lNedD1tIjtCrfRJv4bhh8NMByY0wKlGmM9uaZZHSGOM3jjCy9awG1Ap0MPJffUU1HVj4G/BF2PMWa7TRGRawZmD5Qb9roh6FqaXaN7bhvri/si965ozjqNiWYjuaY5faA1NQctOPgQatasCbqWuJXYqycDJk1SJz09XxxnZ2Bl0DUZY5qFAK8BR90w9QYmLZgUcDnbr9lGbmM5733oNboKcXpj7VqmBdhIrmkuuwAHFb39tgXc+oRC9PznPwl16iTiOKdjAdeY9kSBM111V169x9XaLa1b0PVss2YfuY3l8/tAnCTgN81zQWO2ZCHXNJfLAfKftE1s6pN71lmk7borwJ3AW8FWY4xpARsdcc7LSMyQG/e+MehamqxVwm3E3MlQsExR9xIgtXkvboy1K5jm0V1Vl5ZPn5G45LTTgq4lbiUNGMCA1yapJCQs8JcLa58zU4wx4O1aePafpv6J/y74b9C1NKhF2xLqs9dFcPhtAOcDj7bsi5mOxkKuaQ43Azcsv/Qyit97L+ha4pPj0O+Z/5C6664qIvsBU4MuyRjTorJddWeV1ZT1OHbSsbKmLD7buAILtxFJGXDlHCW502xEdsJr+TCmWVi7gtleqeq6v6lavlyLP/gg6FriVs5pp5E2ahQicg8WcI3pCAriuW2hVdsS6lNVAt8/K4iMAPZqvRc2HYGFXLO9ThLHyct/6mnb3awOiX360PXKK1RddxFwXdD1GGNazVvAE/v22pfjBh0XdC2AF27/sf8/gg+30aZvmstxQTAFmPbK2hXMdlHVz7Sqap/5++4nbnFx0OXEHxH6PvkE6XvuCXAQ8FHAFRljWldctC0MzB7IhTtfyGH9DwumLaEh574HvcdUIE4PoCDockz7YCO5ZnsMFpF9i9991wJuHbInTogE3AexgGtMR7SpbeH6va5v9RePHrk9YsAR8TFyG8v0J0GcFODUoEsx7YeN5Jrt8Vfg2iVnn0PZF18EXUvcCWVnM/Ddd9TJyFgljjMMKAq6JmNMYJ4FTr7o/YuYsmJKi79Y3I/c1paYBr/7SUnKmInIrtgENNMMEoIuwLRZIXXds2rWrNGyL7+UoIuJR10uu4xQZqYAv8MCrjEd3R9cdY/9w+5/SJm2aprUuDUt8iJtLtxGVJfB988Le5y/M7A78FXQJZm2z9oVzLY6WBynZ+F/J9mEsxiShwwh+1e/QlWnAM8HXY8xJnDLHXH+NiBrgJw89ORmv3ibaUuozwybgGaal7UrmG31HHDSgkPHUb1sWdC1xJ2+Tz1J2h57qIiMBr4Nuh5jTFxIddWdU1Zd1vfI/x4p+RX5233BNjtyW5fzP4Seo8r8CWht6hMwERG1UBWTiHQGNrT298dGcs22yFHV40u//toCbgydDhtH+p57IiKPYgHXGLNZuSPOVRlJGXLpqEu360LtYuQ2lhlPgzhpwPFBl7INbhKRV2ofFJHsWrfkui4gIneKyN7iqf286FvMdlMRucLfcKj28WQReVRERtb3BkQks4HXjb6lN+abIiJdgaXA0Y05vzlZyDXb4iQRSSp89dWg64g7kpxMt6uvVnXdImxNXGPM1l5V1Y+OH3w8w3KHNfnJ7TbcRsx+DcLVAM3f09HyRgELow+ISCKwES/kLQbWAxfHerI/2vlboBeQ4j+vrtuhddRwJTA6xnEXOBfo3MB7mNHA60bftgr0sajqWuAD4IrGnN+cLOSaJlPVs93yci16592gS4k7eeeeS2LPniKOcyOwLuByjDHxR0XkckHcP+7xx0Y/qd2H24jyjbDgPVA9BOgWdDmNISLniEgBcARwsYgU+Ld0Nq8SsaeqZgPTgPI6LnUyXhh+Bajyj+2kqhJ9AzYAlXVcoxIojKotXURCQGSmY9g/nlzHiHI1cGmM1zu71rGn6qkhlv8DXhORVp2obiHXNNUIEdm96M03RcvKgq4lroRycsg7/zxV1/0JuC/oeowxcesHEXlwt267cVi/w+o9scOE22gzXwYRB5gQdClN8J2qJgC5wEggCy+odvUf3xh1bl19qRcAT6iqizfyWp9Nj4tIgoikivc9A+8XqUQRyQJK8AJu5PxPRUSBCrxlQGtryrIf4VgHReRFEdHoG95I7p2AW/sxEendhNdsEgu5pqnOBCh49b9B1xF38s47Fyc1VcRxrsf7bdgYY+ryJ1fdkktGXaIhCW31YIcMtxHz3oLqMkW1rbQsVAGIyEXAfyIHVbUaL6ivU9XV/uEw0F1E0kQkJXKuiByPF46X+oci+axT7V5YQNgyvx0IlPnXHgg84de0O5AGhPzRV4D9/PupxG6pa8pySXWdWw1MAoY1cLvWP78pI8JNYiHXNIWo6sTqVau0fPr0oGuJK6HOnck59VRV1R9oZJ+SMaZD2+CIc2f/rP5y5A5HbjrYocNtRHUZzH1DENkb6B90OU3wGXAskAcgIuOAW9nyk703gD8BpcDl/nkJwC21rhUJwJ+zdS9sbtTjAFOA3kAXYBFwCV5f73Sg0h8Z3oKqVgDVMVoWFLi31ihsHvBErWNn1vN9qAYKVXVufTdglX9+zBHh5mAh1zTFLiLSr/i992zzh1o6n38+TkqKiMifadpvwsaYjuv/XHULfrPLb3THnB0t3Eab+VLk3kkt/VIikuH3qDYqE/krHySKSGr0cVX9Ea/3NNJzuwz4GC/oRs75B97oag7wT//wn4BsYH7UeaW1emDBH4X1b29GnVuhqitUdT3ez59SVV0JvAeEo4IpwGdRX4eB12q/PRrfk1uXpobWFvuZaTuemaY4DqD4/feDriOuJHTrRvbJJ6mqfisitf/BMMaYuhQ44jzQq1Ova14++mXvSFtf57a5LPwQyguUlKyTEbmtpV7G//h/Y9TXTXn6JyJyJFFZSlX/ENVjWg7MA24WkdrB70NV/dBfXuuPwK/w2wf80dUaVa0zLPqTyZJUNdYktgNF5By8XxDy2TpEbsSbJDeDrfuDm2PwMwRkicjQBs7r4f/ZYgNnFnJNo6nqcW5hoZZNn2EjuVHyLrgAJylJ8H4bt4XAjTFNcZeq/lFAeP0y+PbpoOuJD+FqmD1JGH3WzsBwYHZLvZJ/7Wr/1phRSAcvPy0CbgCuBogaLY1IAfbBq/9t/1gIr6VhKnjLa4nIfqr6pYhEemTvBi6MEbg/q3VsCX47h4j0AU7DC46/BG4HVuO1K2wxR8S/RgneCG3thvAEINUP/5ueAqTVOpZUu7goucBR/vtsjBbLohZyTWMNFJGdij/4AMIt1j7T5iT07EnOxAmqql+JyJsNP8MYY7awVkRuBa7FtfmqW/jxVRh9FngbQ7RIyFXVYmDEtj5fRJKABcAEVT3M/7orsExV54rIeGA5cLWqLvS/3h/4MKqGL2td9ga8Ht0w3iSyHGAPYA3wEPAgXjgN+TWc6Z+3wn/O71T1SfE2JDq3jtHpz/w/bwJujDqeAvzDv0X7l3+LVtf8ky7A46p6bh2P49e9N3AzLTg4ZD25prGsVSGGzhecjyQmiojYKK4xZlvdjrrF7HeV0ri20I5hyVSoKFRUjwm6lLqoahVej22JiPyWWr2qfp/s62xeSeAi4DFVrXNFAVVdp6rLgV2Bg4Df+A/dijepLEdVl6vqEv/4//A+SRwErI261B+BIcBgvKAcuYHXrtATuKvWy3cGTtnWnly/jWIkjfilRFU/V9VD/O9Ri7C/TaZRVPU4t6xcS6d+HnQpcSOUnU3Wccepqn6N1+BvjDHbogBx7qHzYGF43Oa51ufWwE/vCCJj8FYPiFd9gZXAO3hLhvWo9fifgVNE5E68Udw7Grqg38/6H+A6VY0sZzQdeAB4V0R+ETlXVTeo6i21g7MfHp8E/qKqBZGb/3CJqq6K+hoR6QVkAHMa9a5jO9C/xpTtuEazsZBrGqM78IuSTz8Rrapq8OSOIvukk3CSk8X/h8tGcY0x2+Mu1C1nv6uCriO+zH0jcu/oIMtowEjgJ39ZrHvw1qDdRFXn4AXWK/B6sNdufQnA630Vf+mxz4CXVfX2Wuf8CXgLb1OHp0SkUwO1/Qc4SUT2acT7GIu3W9oPjTi3LlfhheSvtuMazcZCrmmMY0RErFVhM0lMJPe0U1VddwW2Lq4xZvutR5wH6L4TDB4XdC3xY8H7EK5S4jTk+hs67A98AaCqV7J5/dfIOYfjbdk7BbhSRE6o43IOkAwcA/wbr7WBqKXNRFVdVT0fuBBY4fcU175GtEfxVnjYsda1YjkTeD3GurpOjOtmUmvTIxGZgNcGcbuqxsXAj4Vc0xjHaXU1JR9/EnQdcSNz/HgSOncWcZy7sd3NjDHN405Uw+x+XtB1xI+qElg8VVB3LJAedDkxnI03+jkdQESuwguoK0Skq4jcA7yKt2Xv/niTxl4WkQ9jjK6m4O1OdrGqXhkVNpOjHgdAVR9X1UifLyLyKxF5AK91Ym3UedXA7qr6uIicjbe1Lmy5zXBkEtgheBPYakvyb4jILiKSjzdP5/Oo56fgTWL7EoibJUIs5JqGZKnqQaXTpuGWlARdS9zIPfss1HXLgEeCrsUY026sQOR/DD4UsuK5BbWV/fQ2iJMEHBx0KTHkAvdGjVyWAT/ijcbeAYwC9lDVZ9Xze2AcXmDf4oeqqg5T1dorGKCq5f6Er/rmfiQA++FtMPFOredHRnuHASfirZJQu++2GG8E9qMY1x4PTPbvz8RryRirqvdGvUYFMBo4sb71fVubxMmIsolfJwIvrbrxJgqefz7oWuJC2p570O+pp8DbqvHSgMsxxrQv44E3+Pg2+PjWBk/uEHJ3gMu+BXgY72P6uCIiTqytc/1NHari5aP7jshGck1DDgEonRIXEyXjQu6ZZ+L/o3V30LUYY9qdd1B3ObudoTi11+nvoPJ/hvULFHV/SQvujrWtYgVc/3ilBdxgWcg19VLXPbRq+XKtXr486FLiQkLPnmQceCAiMhlvAXBjjGlOYcR5lMyewqBDgq4lfix8XxCnJ966r8Y0ioVcU58B4jg7lE6dGne/OQcl+9hjEccB72MzY4xpCY+j6vq7fRmARZ9G7h0UZBmmbbGQa+rjtSp8/kXQdcQHEbKOP07VdVdTq7HfGGOa0TJE3mTwYZDZc6sHP1pUg9xUtNWtxm36J+PfrAyTcHMRiwu2/MT9w0U1nPd6OSe9XMb9X1dturarysSXyujxz2I+WVyz6fwHvm7hNdQXTwWvK2Bsy76QaU8s5Jr6HKKuS9mXtbfV7pjSdh9DUu/eIo7zNFDT4BOMMWbbPYwTglGnbfXA9FVhxvR0+Pr89C1uCU7TPnSrDivnvFZOuFY2fm5mNUc+W4Yj0CPD4er3Kzj7tQoApi0P8/0al9/umcStU7wNttaWuqwqidmW2nwqCmDVD6DuQVh2MY1k/6OYuoi67kGVc+cSLigIupa4kHX88ZG7sdYRNMaY5vQW6q5itzOVWuv3z1gVZo+eIcbUujXVLZ9WsqRwy3BaUaNc9nYF/xqfwsNHpfJ/h6dw//gUnp1ZTVGlsrhAGdPT4fhhCSwq8NLxozOqOXdU0ra/08Za/BmIkweMaPkXM+2BhVxTl2HiOJ1Lv4qLnfkC56SnkXnYYaqq04C5QddjjGn3ahDnUbJ6CwO3bEOdvsrdplAb7fvVYW6bWsU/x6Vscby8Gm4Zm8yZuyRuOtY708FVb+TXVcURwRHZdGxVsUu/7FaIE4s2bUhkfbmmUSzkmrocAFD21ddB1xEXOh12OE5qqoiIjeIaY1rLY6hq9AS04kpl/gaXB76pIuu2InL+XsRpr5azugntAjWucs7r5ZyzayKH7JCwxWM5qcKFY5II+a0PlTXKXV9WsW/fEHlpDj0yHBbku8xdH6ZHhvDirBomjEiM9TLNb8kX4NaA9eWaRrKQa+pyoLouZdOnB11HXMg+/njUdSuBF4KuxRjTYSxB5B12PAIyugHeRDEFdu0e4qUJadx1WAofLqphwkvljb7o36dUsbFcub3WKG5tN39SyeB7S5izzuWFE1MB2KdviOJK5Zjny7lgdCKfLqlh/34J9V6n2VSVwIoZoO6BgC0ibBpkIdfEIuq6B1b+NB+3sDDoWgKX0KMHaWNGI47zX7w9yo0xprU8gZMAw48GYHTPEDMuSOfho1IZNzCBM3dN4t/HpTJlaZiZaxreTXX2ujC3fFbJE8ekkpFU/0S1MT0d9u0bYnGByzM/VAOQkiDMuDCdZVdkMCjXYa/eIS59s5y8fxTz3Mzq7X+3DfH6crOAXVv+xUxbZyHXxDJEHKdr2dfWjwvQ6ZBN26W/HGQdxpgO6U3UrWLoUQBkJgujemw5iLlPX+/r71bXH3LDrnLOaxWcv1sSB/RvePR1/OBEnj0hjX8cmszV71eyzJ+klhQSenZyeHZmDfv1S+D5WTXcdVgyf/64clveX9Ms+Txyb8+WfzHT1lnINbHsCVA249ug64gLnQ45JNKq8HbQtRhjOpwSxHmH/vtAag6LNrp8XyvMbijzVjmoaGBhw2VFypcrwtz7VdWmtXUH3F0CwIC7SzhrUjnVYWVJrTVzj9kxEQXmbdh8fEWRS+c0YU2Jy455DieNTNxqrd0WsXJG5N4eLf9ipq1rpUYa08bsBlAxa1bQdQQulJ1N2pgxiOO8A5QGXY8xpkP6L07CUQw5nIcmP8HHi8NMOy9904NPfe+1CezVu/421Z6dhG8vTN/i2MpilyOfLeeNU1IZ2TXEF8vDjPt3GfMvzaBPljcONj/fC6/9o1ZQeGRGFReMTmLRRpemb0GxHcryYeNiJbvfHohtxmnqZyHXxDI6XFys1UuXdvh/QTIOGouEQgD/DboWY0yHNRlVl2FHOeeM+g/3flXFSS+XcdjABL5b7XLf11VMGJ7ATt1CbChzWVSgjOrubFohISIpJOzafcsgnJ3inTO8S4i+WQ69M4Wdujkc8UwZtxyUjABXvlvBUUMSGJTrhdyKGmVjOfTs5BB2Yd56l2dmVm8RglvUiulCdr+hQBY2T8LUw9oVTG0hdd3dKmbN6vABF/xWBdUwMDnoWowxHdZ6RD5h0ME6pHsn/ndyGj9tcLnojQreXFDDnw9I5pnjvdUPJv9Uw+6PlFK8jbvsOiK8flIaO3VzOOe1ci56o4KjhyTy7Ampm855d2ENp+zkjZH1yXI4eWQCV75TwU0HJm/3G22UFdNBRIDRrfOCpq0S1Vb9oMHEv2HA7A2PPcba2+8IupZASVoaQ6Z9oU5S0sfY4uPGmGBdBtzNcyfDvDeDriVYffeCc94BuAa4LeBqTByzkVxT22iAitmzg64jcBn77YuTlCRYq4IxJnhesh18aMBlxIFVP0Q2hbDJZ6ZeFnJNbf6kMwu5GQccELn7WpB1GGMMsAB1FzJ4nH38Wl0Ga+eAuraMmKmXhVxT2+hwaalWLVkSdB2BS997b1XVucDSoGsxxhjEeZOs3kKXoUFXErwV00GcnkDPoEsx8ctCronmbJp01sF7tZMG9Cexe3cRkfeDrsUYY3xvAdayAF7LgmfnIMsw8c1Crok2WBwnw9bHhfRf7B25ayHXGBMvPkbdSgZZyGXd3Mi94UGWYeKbhVwTzZt0Zv24pO21F6rqAh8HXYsxxvjKEecj+u0NCSlB1xKsdXMi90YEWYaJbxZyTbRRYDudIUL6HrsrMANbaNwYE1+mEkqE7iODriNYZflQuk6xkGvqYSHXRNtRa2qoWtqx51klDxpEKDtbROSToGsxxphavgKgl+2DwNo5grojANu8yMRkIddsoqqDqpYtg3A46FIClbb77pG7FnKNMfHmG8BCLnh9ueJkAL2DLsXEJwu5JiIEDKxe0rFHcQFSR+8WuTslyDqMMSaGfFTn02tMx14CB2Dtpsln1rJgYrKQayJ6i0hS1VJbHzd15EhV1fnAxqBrMcaYrYh8Rd5AITUn6EqCZSssmAZYyDURgwA6+iYQTkYGSf36iYhMD7oWY4ypg9eX23NUwGUEzFZYMA2wkGsi/JDbsdsVUoYPi9y1kGuMiVf+5LPdGjitnSvLh7J8BYYEXYqJTxZyTcRgoMOvrJAyYtOAgIVcY0y8+g7VGpt8BmxcLKi7Q9BlmPhkIddEDNKaGqpXrAi6jkBFhdwZQdZhjDH1qAC+t8lnQMESQHoAyUGXYuKPhVwDeMuHVa9YoR19+bCUESNQ1YXYJhDGmHgm8hUZXYXMXkFXEqyCpSAi2OQzE0NC0AWYuOCgOqhqyZIOvaC2k55O8oABEFmH0hhj4tdXwEX0Gg1F7fgTuKQMyO4DWX0gu+/mW1YfNLsvktE1cuZ44NsAKzVxyEKuAegljpPc0VdWSB46NHLXWhWMMfFu885nc14PuJTtkJLthVg/uG4RYnP6IXUskxYOhymvVipKKumckQyWZ0wM9j+FARgIULV0WdB1BCqpf//I3dkBlmGMMY0xD3Ur6TYivntR0/Igu1+tINsHze4H2X2R5E5bPUVVCbsuZVUuGzeUsqaogqX5Zfy0pphZK4r4fnkhJZU1AOzYrRPvXLE/eBsaGbMFC7kGoBtAzdq1QdcRqKR+/SJ3FwRZhzHGNEIYZCG5A4LrRRWBjG5bjcCS3RfN7gvZfZDEtK2epqrUhF1Kq8JsXF/CqsIKlqwv5ae1JcxcXsiPKwqpqHEbVcKqwvLI3T7N98ZMe2Eh1wB0BQjn5wddR6CS+vVFVV0RWRR0LcYY0yCRhWT3G44TArcFJg07IejUY4sRWLL7beqHJasPkpC01dNUXarDSkllmA35xawqrGDR+lLmrSlm5rJC5qwupJEZtkFFFTWUVtZoenJC7+a5YssSEVFVWxUjBhHpDGxozu+PhVwDfsit2bAh6DoCldS3L6guQ6Qy6FqMMaYRFhJKhMze/lJaTRR57hYTu/yR2Kx+kNUTcbaOCeq6VIWV4vIa1pcWsWJjOYvXlzJnVRE/rCjkpzUlzfDWGm9lQbkM6prRx1tkIe7dJCIjVPWE6IMikl3rvHJVjfmzSET2A+aq6rqoY5lAD1Wd11ABItIJeBi4TVW/jzp+CTAGuLCu1456rcauzlWtqqWNqKkrsBg4GXitkddukIVcAzaSC0BSv/4qjjM/6DqMMaaRFgKQOyB2yE1IgazeW7USbOqJ7dQdka2zirouFTUuxSU1rCsuZUVBOQvXlTBnVRHfLStkaX5ZS7+vJllVWMHArhl9BQSI91HSUcCc6AMikghsBIoBF8gA/gDcWcc1HsWbO3Jc1LETgUdEZFdVndlADUcBJwEPiEgSXhBVYFdgVCTgivdbQ+Tx6LH3GfhzeRrhHeDwhk5S1bUi8gFwBRZyTTPrpuEw4cKOuzRsQpcuOGmpAljINca0FYsBGHok5A3aqic2anmtLbhumIoapbCwkrXFlSzbWMbP60qYvbKY75YVsLqoohXfwvZbXVSBI5IKZBKna5yLyDl4oTUDOEhELvAf6gVERk33VNU5IjIFKI9xGURkJN42xudEHXOAK/EC8gSgoZB7MvAFsAPwiX+N6Neo/YvCTsCPUV9XA5eq6n1Rz1kP/E5Vn4w69iQQe3mM2P4P2KU5Wzos5BqAruGNGxXVNvFZT0tI7Ns3ctcmnRlj2govCO1xwRYHI8trFWwsY21RJcvyy1iwroQfVxTx/bIC8suqgqi1xWws3fR+conTkOv7TlUPFJFkoAuwDKjC/zQVbzQ3oq6QdwkwQ1WnRh07DegLHAy8JiKfqep7sZ7sh+QjgfOBV4B38UJ2GHgeKAXO9U9PwAvlK2tdpqaB9xktZrO4iLyIF8hjuTNG60kfVV3ehNcFLOQaQFW71qxf32EDLniTznwWco0xbcVXAEvzy/jPtMX8uKKIH6KW1+ooCsurI3dzgXidOFwFICIXAQfhfSyPqlaLyARgnaqu9s8NA91FJA1wVbXCf24P4HTgn5GLikg/vBHiq1X1U7+v9r8iclwdQfcGvLaOqapajNciEblWBrBYVQuizl8f4xpNmTZY17nVwCTgmgaefxzwNzaPdjeJbetrQLVbzYaO3Y+b2K175O7SIOswxpgmKHZV168pquDhTxfx+cINmwJu+aIZrHjovCZfsOTHD1j5+CUsueM4Vj5xGeUL694A0q0sY/n9Z1My833AW1Vh3aTbWH7f6VQs3fyJefG3bza5jqYoKNsi5Ma7z4BjgTwAERkH3ArcF3XOG8Cf8EZVL486fguQhh8cRaQ3Xs/ru6r6AICqPgP8BXhLRG4Wr40D//wDgYnRxYjIRSIS9tsNhgInish6ESkUkTV1vAcF7hURjdz89/NErWNn1vN9qAYKVXVufTdglX/+Ni0fYiHXpIrjZITzO/bKCqG8Tf821vWX2hhj4o4jsrRnduoWH21XrV/K+sl3oG7T1ukq/u5tNrx9LxkjD6LbSX8lpfdw1r5yM+WLYm8CufGjxwkXb5rgT+WKeVStW0SnMUdTOO0lAMKlBYRLWnYQpaB8i3aFFiEiGSKSLLFm6sU+X0QkMTpkAqjqj3i9p5Ge22XAx3hBN3LOP/DCbA7+qK2IHASc5Z+PiOwPTMMbuT6r1mv8HbgY+CPwjV9HJ7wJawtrlVoJLFLVztE34FSgruZswevJlcgN2ACcXevYU/V8i5oaWrdp0TlrVzBdADr6SG5Cbl7kbqyPZowxJl4t656Zspsj4CpUrpzH2pf+TEJOD8KljW9PVVUKpz5H5m5HkbnH8QCk9B5O1dqfKfrqv6QO2G2L88uXfE/JzPeRxM0broWL1pDUfRBpQ/beNLpb8sO7ZOw8rhneZt02bh7JzavvvG3lL++1Merrpjz9ExE5kqi8pap/8EdhwQu784CbRaR28PtQVT/02xaewft4P/ILzTDgA7xR2xkicrSq/uzXtxwv5O4OdPZbIkbgTcybCHwU9RouMDDGZDOAutala44B0hCQJSJDGzivh//nNrVUWsg1/vJhNpKrrlsgjlPd8NnGGBM3VoYcIS89mXUllVQsm0XO2HNBhIIpzzb6IuGSfMIlG0jpv+sWxxNze1Ox7MctjrlVFeS/dQ9Ze55AyazNeUlVvSXJREBdNFxDuCSfhKzYqzw0l8IWDrl4o46z8T5ir6Zxo5AOXsZahNcHezXEXLkgBdgHGA687R8L4bU0TAVQ1TIRORf4FrjXP/YQ8JCI7ACMYMsJd9mARK+Bq6rf+YEyVkBdqKqDog+IyC/ZsoUiWgKQWmttXwHSah3beqeQzXLxljI7tp5zar9mk1nINZ0BwhsLAi4jWAl5eVB3/5ExxsSrDQCZqYmsK6kkc49jEXE2jaQ2ljhe9nErirc4XrV+CaFOW2bHgk+fQpJSydrn5C1Cbig9h+qNK6nesJxQei6lcz8jbei+2/SmmqKoYlPIzWyJ6/sTtEZs6/P9tWgXABNU9TD/667AMlWdKyLjgeV4k8cW+l/vD3wYVcOb/rVqXz4ZL3hHfxxbQ4wVEFQ1399VrLZ+IrK41rFU6ljGDC+Y/8O/RfuXf4v2Sh3X6AI8rqrn1vE4ACKyN3Az27j+sfXkmjQAtzy+FvdubaG8PBWRtUHXYYwxTbQBICctESDm5g6NEUrPISG3NwVTn6em2OvaKv72TapWziNt8F6bzqtYPovi796m85FXIqEtx8lSeg9Hq8pZ9+otZOx6OJXLZpHSZ+Q21dMUpZtXk+jU4i+2DVS1Cu9nbYmI/JZavaqquh54HbjWP3QR8Fh9u45F6YS3MsP2rCu7RFX7R9/YvIxYLJ2BU7a1J1dEQsBIvNHxeqnq56p6iP89ajIbyTXJAFrZvtZNbBLHIZSVJYCFXGNMW5MPkO2H3O2Rd8SlrH3pJlY8eC6SkIxWlSFJqaSPPBgAt7qSDW/eTdYvJpLUbYetni8JSfQ4627CZUWEi9ZBuIb89x6kdPYn5B76a9KHH7DdNcZSWrmpeyAuQ66vL956s+/gTSarvZvZn/F6awvxRnHrHeGMMpCt17Ftqkb35IpIL7y1c+dsfXqjHehfY8p2XKNROsRIroh097fNQ0T6xroFXWOAUgDcym1agq5dCGVnRz6qs5BrjGlr8gFy0uprf2yclN4j6P2bJ+n8y6vI3MPbMTZzzLGEUjIAKPzsPzhJqWT9YmKd15BQIgmd8iid8wnJfUZQOudTcg4+n4Ipz2x3fXWpCrtU1bgQ3yF3JPCTvyzWPXjtAJuo6hzgP3jr596lqo39eXQU8N021BPd97AwegTWH4U9qo7njcXr//1hG14z4iq8kPzVdlyjUdrtSK6/zMe1wG/xGpx3whsaX8zWvR0usP2/BrdNKQBa1XFHckPZ2ZG7HXv2nTGmLSoEyEhpnh/nTnIa6cP2Z8O79+OkZm4KuwCl86YSLlrL0tuP2eI5G968i4Ipz9L7oscBqClej5OaSbi0gMTc3t713rqnWeqrS2lVjSYlJMVlyBWRFLzR2esBVPVKERlc65zD8bbbnQJcKSI/qGqsftZQ1HP2xVst4cAmlBP5bSjH3/2sG5AoIoNqndcDSBCRHf2a5/nHzwReV9XaS3o5bD1wmkmtTRz8jS+OAM5prq1769NuQy7eOnRnAXcD09lyF5Qj8Zq8d8NbXPnB1i4ujnghtwOP5ErypiVw6mqyN8aYeFUMkJ7cfD/Oa0ryKZ35PtkHnImTnLbpeNcJN0J4y/lMa1++kYxdjyBtx302HSv5/h0ydjmcmsI1bON8oSYrrawhJy0+Qy5wNt4vI9MBROQqvC1tV4hIV7zwe55/ew5vQtfLIvIRcEOtLXyTgZCI7Am8CrygqrU/9k+k7nwX+YG3E14+qsIb6Ktr149v/LoP9CeBHYK3Y1ttSf4NEdkFb5myHOCyyAl+2L8J+BJ4uo7Xa1btOeSeDlykqrHWUFnpL8j8o4gU480G/FurVhc//JHcjhtync0ht66Fr40xJl4VA2Q0IuTWFG/ALSuM2U8brXDqc4Q65dFp1JFbHE/qHKOzz0kgIbPLpse0pgq3ooSETnmgLtX5Kyid/XGLLyNWUlkjqtqpiWvYtpZc4N6okcsy4Ee8tWzvAAYAe/i5BOD3IvIu3iBcSa1rJeD93O6M1zJwQYzXS/bP2YqqLmJzq8JDTXwfxcDtqvpRjMfGs3mjiZl4LRkfq+rHUa9dISKjgTxV3aYdzJqqPYdcZev/OcBbQDl6VDcbf4WBDsrvye247QqSvKmXzUKuMaataXTILfn+bYq+eZ2+l79Q5znVG1dS8sO7dD76D1utntAY5Yu+JX34gQAkZHYhfdj+bPzwUXIPvajJ12qKmrBC1Ef58URV/xq9U1pkC14AETkfqKr90b2qvge8F+Na0TtrvFHH67VItlPVmcAf6njs06j7LnBjHeeV432S3irac8h9CrhfRNKBd1Q1Hzb3lYhIFnAccBf1bz3X3vmrK3TckVxJspFcY0ybVQKQlrRlvsvY6RAydjpki2PZ+55K9r6n1nuxxJye9Pv9a41+8UgfbkTa4D23+Dr30F+Te+ivG329beWqopAQl+O4bAp+sY533B++raA9h9yr8X6rexKveboYKMLrPUnH+/hA8GYzXhVQjfGgw/fkWruCMaYNqwYIOfEa71qH68bvSK4JTrsNuapaDfxWRP6K1yg9DK+HJRHvN98FwHtRMwY7qg6/uoK1Kxhj2jAXIBSfvaitxsu4HWNZVNN47TbkRvhrzdW5gbd4Xep7qOqXrVdVXOnw6+SKjeQaY9ouF8Dp4CO5YbWRXLO1dh9yYxGRAcA44FC8hY2z6bh/OZIAtGarba47jKie3I6b9I0xbZWqqh64Yxf55PcHBl1LYLplpiCQFXQdJr50iJDrTzI7iM3BdgDeb7/fAk8An9b97HavGkBCIbS6OuhaguFuWsmko/6iY4xp2yQtKYF+eR3iR3qdVDW54bNMR9Ju/0b4O4Ecihdsx+BNMvsOmIS3bd4vVLWuxY87kioASUxEKzrmp/VR/cjbvy+mMca0MhFh7ty5vPzyy0GXEpgzzjiDPn36rA+6DhNf2m3IxRudVbwQdyfeAsbrAUTkSv+4iQq5HVXUCLaFXGNMWyMA4XCYmo7cduZNvOu43wATU3ueiXgi3m4ey4HfA4tF5B0RuRYv/LbOXoPxz0KuhVxjTNsVAnDdmMuwdhh+yO3Y3wSzlXY7kquqr+Lt64yI9MdrWzgEuBLvN99PReRz4DNgSoy9nzsKC7mbQ671cxlj2poQQK0NszocP+S2ylaxpu1otyE3mqouBh4GHvaXDBuN1697KN7Wc4l03ElHlWAh12cjucaYtiYNoLqjThz2OY4D1q5gamm37Qoi8icR6V77uHq+UdVbVfUgIAc4svUrjBveSG5Sx813FnKNMW1YNkBFB504HGEjuSaWdhtygT8DPRs6SVXLVfXtVqgnXlm7gq2uYIxpu7LBQm5iYiIi0rG/CWYr7TnkCja5rDE6fMh1y8ojdzODrMMYY7ZBNljITUlJUWBj0HWY+NLee3KfFZHyhk8DVd2tpYuJU367QscNueGN+ZG7nYOswxhjtkE2WMhNSUkRoCDoOkx8ae8hdz6wIegi4lyHH8kNbyyI3LWQa4xpa7KhY4fcxMREQqEQWMg1tbT3kHujqs4Iuog4VwHgpKQGXUdgtLqacGmphtLTLeQaY9qabOjYITclJSVytyDAMkwcas89uaZxNgA4WVlB1xGocH4+QJeg6zDGmCbKBgu5voIAyzBxqD2H3LHAvKCLaAPWAyTk5ARdR6DC+fmirmsh1xjT1mSDhVxfQYBlmDjUbtsVVPWToGtoI9YBhHI7eMjdWAAinbFVOYwxbUseWMj1FQRYholD7Xkk1zTOeoBQBx/JrdmYj4gkARlB12KMMU3Qr6ysTDvyjmcWck1dLOSaUlWt6ughN7xh0zJiW+2SZ4wx8UpVB2zcuFGCriNIFnJNXSzkGkV1XUfvya1esSJyd0CQdRhjTBMkAT0LCgqCriNQFnJNXSzkGsRx1oVyczt0H2rVsqWRuzsEWYcxxjRBHxERC7kWck1sFnINwLpQbm7QNQSqaumyyN2BQdZhjDFNMADAQq6FXBObhVwDsD6Uni4dedez6lWr0HAYLOQaY9qO/mAhNzU1FVVVoCjoWkx8sZBrYNMKC9kBlxGg6mqqV69WVbV2BWNMW9EfLORmeZsZrQTCAZdi4oyFXAORtXJzOnbLQvWSpYLqILy1co0xJt71Bwu5OTk5KiKLgq7DxB8LuQZgLUBCl85B1xGoquXLEMdJBzr2N8IY01YMKC0t7dBr5CYlJZGWlibA4qBrMfHHQq4BWASQ2LtP0HUEqnrz5LNBQdZhjDGNIKo6ct26dR36kye/VQEs5JoYLOQagJ8Bkvr0DrqOQFUuXBC5OzLIOowxphH6i0jm6tWrg64jUNnZ2ZG7i4OrwsQrC7kGYImqhhP79A26jkBVzJ0XubtLkHUYY0wjjALo6CE3Z/NGRtaTa7aSEHQBJi5Uo7osqU/v/s194fU1NfxlzWqmlpYRRtkzLY2/dO9Bl4QEVJU7169jclERxeEwQ5KT+WPXbuySmtro61e6LretXcvbxUV0CoW4onMXjsjMBGBFdRWH/vxzzOf1TEjg/YGDeDx/Aw9v2MBFeZ05EwgXFem/X3117Nlnn91NVdc0x/fAGGNagIVcbCTX1M9CrgFAHGdhUr9+/WjGlQVUlctXrmBldTW/7dIZVbh/w3quXrWSx/v05YWCAiYXFXFd125kOA7PFmzk3GXLeGuHHeiS0Lj/Nf+8ZjUfl5RwTdduuCjXr15Fz8REdklNpUtCIi/267fVc25bu5bcUIgaVf61fj23dO/Bn9as5rScHCrmzZMpU6YMVtW1zfV9MMaYFrBrOBxm3bp1QdcRqOzsbFTVFZHlQddi4o+FXBOx0ElLOziUm0s4P79ZLvh5WRlzKiqYPGAHevobTSQ7wk1r1lAYDvO/4iJOz87h0E6dANgtNZXd5//EF6WlHL15MkGdFlVVMrmoiDt69Nw0eru8upoHNqznwd59SBJhZMqWo8ILKiv5obycV/oPID9cQ6dQiCMyM7lt7Vryw2GW/HcSY8eOTcTb3ndhs3wjjDGmmanqbmvXriUc7thLw/ojuSuBqmArMfHIenJNxEKAxD7Nt8LCzikpvNCv/6aAC5AdCgGgQEE4jEadX4O3kney07jB5C9Ly0gR4RA/JAMcnNGJr8rKCKvGfM79G9ZzWKdMBicn4+rmvwCOgKvKy2+/zcSJEwHGNPZ9GmNMK+siIj07eqsCQHZ2tq2Ra+pkIddE+CssNF/I7RQKMSg5eYtjn5WW0j8xiexQiL3S0ni2YCPzKiooCoe5Y+1ackIh9k1Pb9T119TUMCApiUTZHIp7JyZSocqampqtzl9eVcW7xcWcnettepEbClEYDrO8qoqicJhyVXJLS0j0QrmFXGNMvNoVrB83ao1cC7kmJmtXMBELoXlDbm1Lqqp4vbCQa7t1A+B3XbryTVk5xy1ZDECKCP/u2490J9So61WoS6fQluemOd7vbRvD4S1GkAGeKyhgl5RUhqekAJDkOByRmcm4RT8zISubyUWFnJKbh1tWpk5amoVcY0y82hVg1apVAZcRLJt0ZhpiI7km4meAxL4tE3Jd9SaF7ZCUzAlZ2QDcs349RW6YP3Xtxt979GBYcgq/Wb6cpVWNa61KEofacTgyplvhulscr3JdXi0s4KSc7C2O39K9B+/tMJCru3al1HX5vKSYzt27y6WXXrovkNTkN2qMMS1vL9d1WbOmYy8AYyHXNMRCrokoVNfNb6mR3Efz85lZUcFtPXqQKEJ+TQ3/3pjPvb16c1JODkdlZvFEnz6kOMITjZz4lhcKsbpWW0KBPwkj1dnyf+1PS0upUOWgjE7U1isxkclFhRydmcV969dzyzHH8vzzzyc8//zzh2/j2zXGmJYiqnrA6tWrqaysDLqWQHXp0iVy96cg6zDxy0Ku2UQc56ekHXaIPWNrO0wrLeXe9ev4Q5euDPVbBZZVVxMGBidtHixNchwGJiWxvJH7sO+YkszSqio2RAXdWRUVAHSttQTZ28VF7JOeTroT+3/52RWVDE9JYVV1NePCYYYMGUJZWdn4prxPY4xpBcNEJG/JkiVB1xG4Hj16oKoK/BB0LSY+Wcg10WYm5OZKqHPnZrvggspKLl+5gsM7ZXLK5p1pNq2yMNMPpQCF4TDfV1RsFVDrsltqGlmh0KaRX1XlmYKNDE5KpnPUNapV+biklLHpGTGv83lpKXunp236unzmTNR16d69+6jGv1NjjGkV+wMsXrw44DKC1717d4AFQHHApZg4FfjEMxER/zcxU4uIdAY2tOL353uAlKE7Ujpl/XZfrNrfDCJRhF9lZ/NjRfmmxwYkJTE4KXlTAE4Q+KCkhMJwmJP8PqsqVX6qrKBvYhKZoa0noyWK8PsuXblm9Sp+rqqkyHWZUV7OPT17bfmmysspU5fd0mLvpPZ+STHXdfUmw/VITGTy+nXMW7aUnUaO3BmvL9fWXzTGxIsDAD766CMeeeSRmCf069ePs846q8ELzZ8/n08//ZRVq1aRnZ3Nfvvtxy67bN7VfNGiRTz99NNbPe+GG27AcRzefvttfvjhB4488khGjBgBwLfffsvOO+9MKMa/2c0pKSmJvLw8ROTbFn0h06YFHnKBm0RkhKqeEH1QRLJrnVeuqpW1zkkD7gH+oqpLoo7vBVwH/FpVV9T1wiIiQA7QGRgGHAYMAsYDiUBYVRsMOCKSSeNHxatVtbQR1+yK10x/MvBaI6+9vb4HSN5xKKVTpm73xeZXVvKzP4nsjGVLt3jsyT59eLRPH/6xdi2Tiwopc136JCXxt+492Mnf1nddTTUTlyzhnp69tlgLN9rRWVlkhUI8nr8BgHt79uLgWud+WVZGdijEgKTkrZ6fX1ND/8QkQv4yZBfndeZva9dw0n770adv3xS8pcQ+357vgzHGNBNHVQ9Zs2YNeXl5nH/++Vud8PLLL9OjR48GL7RgwQKee+459tlnH8aNG8fPP//MpEmTCIfD7LbbboC3ekPPnj058sgjtyzCcSgqKuL777/n0EMP5aOPPmLEiBGEw2FWr17NqFEt/yFY165d8X6E812Lv5hps+Ih5I4C5kQfEJFEYCPeRxAukAH8AbgzxvPPjXH8dmB5fQHX92vgfv81VgKzgW/xgu59wMEiMTcmOERVP4j6egYwsIHXingHaHBCk6quFZEPgCtovZA7EyBlxyHNcrHhKSnM3nFovefc3rNnnY/1Skxq8PkAB2RkcEBG7FYEgIs7d+biOlowchMSOMNfNxe80Hx0VhapvTdNwDsAC7nGmPgwUkQ6L1y4kOTkZHrW+vdzwYIFlJSUsO+++zZ4oU8++YShQ4dy8MEHA9CnTx/y8/OZOnXqViG39usAFBYW0qVLF3bZZRfefPNNAH788cdNI7otLSrIf9cqL2japMB6ckXkHBEpAI4ALhaRAv+WDps2wtpTVbOBaUB5jMtU1foTEbkM2Bc4SUQ06vZyjOdXAvlAkqr2AS5U1atVdS5eeO4JfIk3WpwDHOg/r/bC09XApaoqkRuwATi71rGn/NdsrP8DXpM6knYLKFTVJclDGw6W7V3FDz/gVlYqMDboWowxxncIwM8//xzzwY8//pg999yT9AY21FFVVq1axQ477LDF8by8PAoKCjZ9HQm5dV1DRBARIh11S5cupW/fvo1+M9vD78cFb2DKmJiCHsn9TlUPFJFkoAuwDC+wdvUf3xh17hZ9qSIykc3Loh4uIkvwRn5vBY4GPvMfGwp8ite+UJsLqKqGRSQL+EZE/qiqj6rqEhFJBXYB/qmqBSLi+M9ZVus6W2+vVbeYG42LyIvAhDqec2eMnNtHVZc34XUbRUS+Sx44sJ8kJ6MdeHkaraqifPoMSdtrz/3FcdKBBltMjDGmhR0SDod16dKlW/1AWLJkCatXr+akk05q8CKRcFpevuXY0bp168jMzASgsrKSDRs28M033/DOO+8gIgwePJhx48aRkZFBRkYGBQUFrF27loyMjFYNuOCFXP8Tz4697ZupV5CrK1QBiMhFwH8iB1W1Gi/srVPVyP+8YaC7iKSJSIp/7ETgDP/+CcDxwMHAtao6WVULVLUAr83hCVWd10A9YeArvEDZzz92CVAGvOV/3RlY49cYzaXx6jq3GpiE1xtc3+1a//yWSqDfSCiEjeZC8UcfIo6TDIwLuhZjTIeXpqpjly5dKtUxlln86quvGD58OBn1tG5F69evH19++SXr1q0DYN68ecyaNYsdd9wRgJUrVwJemJwwYQKHHXYYixYt4qWXXgIgNzeXvLw8HnroIcaMGcOsWbMYOXJkc7zPBjmOQ7du3VREZrTKC5o2K+iRXPBGXO8BbgEQkXF4o7G3RZ3zhv/1n4FrgNtUdaKIJOCFw/NVdYGI9AaWichdtV9ERC7w7+6hql/793OBRBH5C3ARXshdDBwqIiuAm4HfAzkicilekJ4f4z0ocK+I3Fvr+BMi8kStY6/U8X2oxmsXmFvH45H3EdnHMeaIcDP4BiB1xAgqvv++hV6ibSj+4EO6X3cdwLHAf4OtxhjTwR0uIilz5279I6K4uJi5c+dy5plnNv5ihx/O008/zQMPPEBycjIVFRWICLvvvjsAPXv25IILLthiEltmZib//ve/WbNmDd26deO0006juLgYEeG7775j2rRpTJkyhf3224+99957+99xHTp37kxCQoJg/bimAds8kisiGSKS7H+E35jzRUQS/RaATVT1R7ze08jnJsuAj/GCbuScfwBpeH2x/6zj+ruyeXSzt3/uc8AT/v3IIq0VUU/7BZAJ7AGMU9XxwF7ALOB14ClVvQ9YDVwJpAN/i/XyNL4nty5NDa1NGT1uiukAKSNbZ/JAPKtZuZKKOXNQ1z2K+PiF0BjTcR0PMGfOnK0emDVrFhkZGfRpwo6VnTt35rLLLmPixImMHetNPdh5553Jy8sDIDk5eatVGiLtCKtXex+yOo5DVlYW06dPZ5ddduGTTz7hmGOO4ZNPPsF1W+pHlPXjmsbbph/c/vJeG6O+bsrTPxGRI6NfW1X/4I/Cghd25wE3i0jt4Pehqn4YXYr/56tACRBZ56RQVUtEZAxwu9+2EKvO64GXgHej3hd4qz2MB76OOrYrXl9mrN7M5mj7CAFZItJQn0DkX52Wmoy2Tl13WcqIES2zv28bU/zBB6QMG5YD7AN8EnQ9xpgOKVlVj165ciVFRUVbPThr1iyGDRvW1J/FJCQkMHToUKZOnUooFOKAAw7Y9NjGjRuprKyMDpSUlZUBUBO1y2R1dTWVlZWICCkpKQwdOpTk5GRKS0vpVMfSj9srqqbvWuQFTLuxraNTYbzltqr9W2NGIR3/9RYBNwBXA4hI7Y0OUvACxXDgbf9YCO8j46n+c1KAC/FaDACeBu7Cm7wGUBz1l/1hEXk46vqbFktV1XkicgnwQiPqj/gV8GKtYwlAaq21fQVIq3UsibrlAkfhvc/GaLGRRXGcr5MHDeojqaloeaxFLTqO4g8+pMsllwAcg4VcY0wwDhaRTrNnz97qgeLiYpYvX75pNLapqqurmTZtGmPGjCEnalfK6dOns3jxYs4777xNx773W9h69+696dgPP/zAzjvvvE2vva387XzLRGRBq76waXO2KSipajGwzZ9ni0gS3lZ8E1T1MP/rrsAyVZ0rIuOB5cDVqrrQ/3p/IDKKWw1cBdwN3AFMUtUaEVnD5raEs4FL8doGngb+7R+vvf1fBTBVVTctLCgi/fHC+KYVDKL6fyvYWgrwD/8W7V/+LVpdPbldgMdV9dw6Ho/Utjder3BL7oL2pYRCx6fuvBNlX37Vgi8T/yrnzKF65SpN6N7tWHGcq2jZ77sxxsRyAsRuVfj5559xHGeL4AneqGtBQQHdu3fHcer+sPHLL7+kpqZmi1FcgFGjRvHVV1/x8ssvM3DgQFavXs3XX3/N8OHD6dat26bzVq5cyejRowmHw1RUVDB79mwqKysbXMZsW4kIPXv2VH+ns5briTDtQiCrK/i7iKUBJSLyW2r1qqrqerye2MhKAhcBj0V2PFPVMN7SXnfXep7rtyYUAmcBz+KNMpdHVlvwnxstDIREJDtyw+vTBciMOpblH4v1l6ozcMq29uSKSAgYiTc6Xi9V/VxVD/G/Ry3lE4C0PfZowZdoO4o/eF/EcQbg/TcyxpjWlKCqx65Zs4b8/PytHly8eDHdunUjKWnLDwp/+uknHnnkEaqq6t60s7y8nKlTp7LffvuRmrrltud5eXmcfPLJbNiwgTfeeIMFCxZwwAEHcPzxx286Z/Xq1ZvW2g2FQuy///68/vrrHHDAAfUG6+3RrVs3kpOTBW9pUGPqFeRkmr54u4y9gzeZrPauZX8GZohIId4o7hYjnKq60R9djeU6oD/wIP7i2Q3Yiy3X5I2Y1dATRaQX3o5sW/+K3XgH+teYsh3XaE4z1HVL0/bYo2V+FW9jit97j9zTTwc4CX9XOGOMaSX7i0hurFYFgGOOOSbm8V133ZVdd9213gunpqZy9dVX1/n4gAEDuPDCC+t8vHv37lv07O67776N2m1te/Tv3z9y10KuaVCQ6+SOBH7yl8y6B6i96sIcvPVzrwDuUtW1jbmoiJwH3AhcWXuzBBHpKyK3ikhOradNrTXiOsA/3ifqWGIdLzkWb+T4h8bUV4er8EJyvPQGVIvjfJa2yy4qyckNn93OlX39DdWrVqm67hkE+3fGGNPx1Nmq0BH169cPVXWx7dZNIwTyA9ufOLY/8AWAql4JrKp1zuHAyXijm1eKyAm1Hu8F7O5/WSMief4Es0eAP6rqY/5jYWCEv3TZSXhLgUX3VQqwT/QWwGzetndZ1LGtV9/2nAm87v+li+aw9fc3s/Z1RGQC3tbGt2tkb8T48LEkJUnqrrsEXUfwVCl87TURx+mNbfNrjGk9iao6YcOGDbp2baPGedo1EaFfv34KzAC2XmbCxDUR6SxNXQJkOwU1KnU23ujndAARuQpvYtgKEekqIvfgLQt2AV4YfhB4WUQ+FJF9/GuMwftN7kO8SWr/wZsBf7yq3hH1Wi/g9fSWATcB/4gsKeZLAL7Em/hV36171Pn4de+N1w5Re8MH8FZSSPLP20VE8oHjiPrt0w/7N/mv/3QD37PW9jFYX25E4WuvRe42frV1Y4zZPkeJSJfvvvuuVYNBvOrcuTNpaWkiIoG1Kvjr/Y+qdSxNRHqISG70/J4Gbrki0jM69PnXeTRq19XI8b1EZLI/uFdfbeJfd4iIHCMi94vIuyKSICKp/iT/xrzHzEa+h2wRaVRbo4h0BZYCRzfm/OYSVE9uLnBv1MhlGfAjcDHeagkD8HYm+9F//Pci8i7ermgl/rG3gc6qugFARM4AtPaELFW9B68doi7JQE1DE7mi+n9Tog4X443AfhTjKeOBhf79mX4NH6vqx1G1VYjIaCAvxoS4oFlfbpSqRYsp++47Unfe+URxnIvZepUOY4xpbue5rsu339qeB7BFP26LLOcoIiPx5gulsnkDqs7+rRewAzAESBKRM1Q1smrTcXiDXZVsXlI1Ga/NMZJZMvGyTmSRYcd/nS5AQVQZ57L1HKXbgeWquqKBt/Br4H68CfIr8SazfwsMAu7DW4ou1vMOUdUPor6eAQxs4LUi3gEOb+gkVV0rIh/gtaC+1tD5zUWC+oRcRJwYH/EjIslAVZx9dN9RvaVVVYfN230P0crKhs9u57J/9St63HQjwDnEHr03xpjm0ldVF8+bN0+ef/75oGuJCxMnTmTYsGGuiHQm9mTx7SIipwDX4K2OtAEvfA4ARuOt9rQKbwfU1cBqVS2r51rX44XHA/2vVwMnRQ90xXhOZKnSwaq6wD92GbVWkvK9oqon1nr+OXiBuKuqhkWkv6ou9h/rB1ThbVH/Jd7k/l3wPrUdqKo/R11nDvAvf8fXyLH1wO9U9cmoY08COaoae/bj1u/vIP8172qtjBfYJJpYAdc/XmkBN25YX26Uorfewq2qUqxlwRjT8s4REZkxY0bQdcQFEWHAgAEKfEPLBNyhwDS8TZnOwpsQ/hfgA7wR2DfwRjhX4o2UdhWRwZEWAhHJqDW35y/AAVFfdwM+ijpnca3Xn4g/yRA4XESOEpEDgVvxPuLP8W+/wAvC18V4Gy7eJ9phEckCvvEn46OqS/BC+y7AFL9t0/Gfs6zWdWpovJifQovIi9HfD/978AHeKLVb+zHZvOttswpyCTET/z4Gry+3o28KAeAWFVHywQeSecQRB+D9dr+ooecYY8w2CKnqucXFxbpgwQLrx8Xb5Sw1NVWA91voJb7Ay0S1Q1sSXpvidzGek4g37+ccvFYF8ELyFOB3eMuD/tI/Pg+vFWEK3ryki9jSiUCkPfAEYDHefKNrVXVy5CQR+QPwhKrOa+D9hPFWbLpTRN7zQ+4leIH9Lf+czsAaVa09sb4pm2zUdW41MAlvZLw+xwF/Y/P3r1lZyDX1sb7cWgpeeYXMI44A7x+oPwRcjjGmfRonIr2//fZbXNc29QI2bTpBC4VcVc0Bb0SWLbPR2XiBtfYur2X+xlaR51f7o5UlqlogIhV4830K/OtGP1ZOrZWWVHViVLvC+aq6wB/dXCYid9WuV0Qu8O/uoapf+/dzgUQR+Qvez6iv8MLyoSKyAm+31N8DOSJyKXA8MD/WtwO4V0TurXX8CRGp3apX1y6u1UChv0xsnUQksrJWi8xLsjU/TX02r5dbazecjqp06udULlqk6roXsPm3bmOMaU7nq6pNOIuyww47oKoV+EuPtqBJeO0QkdudQM9axzZSq23ND6hNGXWva+39yPV2ZfPoZm+8VoXn8OaDRFoXACqinvYLvAluewDjVHU83mZXs/B2kX3K77NdjbecajreKOpWLw9cuq27uPqaGlpb5Lc5G8k1DXlbkpIOT997b0o++KDhs9s7VfKfelp63PjnLOAM4IGgSzLGtCvdVfWohQsXUlBQEHQtcSExMZG+ffviLx1W0eATtk8F3mpIkZ7Xc/E+tdsx6pxZbP3xemTlpY+iVzDwR3CJ8diSGK8defBVvFUZjvS/LlTVEhEZg7eiU4F/7drPvx54CXjXfzzbPz4Hb8Wnr6OO7QqU+rfammMANARk+b3O9enh/9kibTk2kmsa8hpAp0MODrqOuFH42muEi4pUVS/D/g4ZY5rXRSKSYBPONuvXrx8JCQkA77XCy4WBy/CWiSwG7sIbyS2OuvUlauRRRNKA8lqjnGnAm8CjgBP9WGRnVRFJ928pIvJbNm8b/zTeHgGR4Fzsh+XBwMNRE7nAW6oMAL9Pdz+2HnXeiBd8o79e7v95XIzvQQKQGr0eLl4ITat1rL51d3OBY/ECdn23yEhyiwy62g9o05DFqvpDp7FjlVAo6FrigpaXU/DSS+L/hnpo0PUYY9qNDFW9LD8/X+fOrbeVsUMZNmxY5O7rrfByIeAhoI9/uwbv4/0+UbcitsxPs/F2Xo1eSaAMb/T0PGKsJoAXkkuAv+P1r16Ft2MrwCRVrQHWsLk14Uq8yc7f4IXwyPHaPS0VwNTagdp/rE/UscSo82tLAf7BlqE4F/hXrWMn1/N97AI8Xjvcxwj7++CtutAiq2pZyDUNEpFJoexsSR01quGTO4j8Z55Fw2GA3wZdizGm3ThPRLI///xzsQlnHsdxGDZsmPqbQ/3UCi+ZAlyIt6zWMrwlvLpHfb0Mr+81emOoff1zIsHzOrwAu1/UsRPwWhzG+F/n4fXa/tnfDGoXaq2Hq6qu35pQiLes2bN4I83lqlrg32r3voaBUK0R10z/scyoY1n+sVj/o3UGTtnWnlwRCQEj8cJ/vVT1c1U9pKENubaVhVzTGF7LwsEHBV1H3KhZuZLi998HOIIte7WMMWZbJKnq70pKSvS7774Lupa40bdv38hWvnXN4m82/mZUR+It4RYJchcBK2qFu0TgP5GdUFV1uaqu8QPpUXi7s84DhuKFyQzgXrxAO90Pp/mquiKya6uq1rf273VAf+DBRr6VvdhyxPV7//isqGMxQ6W/7m8GXivBtjrQv8aU7bhGs7CQaxrjW3Xd5Z0OOcQ26YiS/3RkR0euCLIOY0y7cLKI9Jo2bZrU1DRlLf72LapV4dVWeLk/422YUCAiBSJSgL+6QuRr/1gB3uhqrJ2+3sQLum/jfZw/G5iLF4w/E5Em5S5/M4cbgStVdXmtx/qKyK0iklPraU1pV6htrP/efmhKnbVchReSA19g30KuaQwVx3ktqU8fSR48OOha4kb59OmUffstqnou3kQEY4zZFo6qXl1ZWanffPNN0LXEDRFh+PDhqqoL2Twpq6VeqyfeaGtPvH/P+/u36/C28+0fdeuNt4PZNBHJjb6OPzL7Ad5mSsvwek0f9o+9DawRkaf9Hc02TdzyR1B397+sEZE8EXkYr0/3j6r6mP9YGBghIqnASXi9utEDUALsU6v/N7Jx0bKoY7U3gIg4E3g9xq60Dltnxsza1xGRCXifcN4eD7vX2hJiprEmARdnHHQQlfNjrR3dMa2/7z76PvZYAt6+5r8Ouh5jTJv0SxEZ9vXXX1NR0dIrZLUdvXr1olOnTgK8TAtNTIryFt7qBRXE7lNdUOtrwVtd4BERuQNvRYT/b+++w6MqsweOf8+d9N5DC733KkVBrGvDjm117XUtq+u6lt2frrvrWta+rr3r6tqxYUGBFUUUFKRID70kENJ75vz+uBMcQughdzKcz/PcJ5k7t5wJJDl557znTQBSgM64k9VeA3qr6irYWg5xDG7N73vAdBE5LLDi2NDAvi9xOx98AAwGTlXVd4Pu+1/cSWG/C8R6b31LsYAIYAa/rLS2I75AjFvzQBEZBRwJNFabGBXYEJEBwGTc2uJrg86PAf4SuP9Lu7h/s7Ak1+yuqer3FycecUTS5ief9DqWkFH29TeU//ADsYMGXSwi/6Dx3ofGGLMjoqq3+P1+/fbbb20J3yDNWaqgqgP25XwR+RA36VsCzAIWNhzJVNUq3DkuE0SkE1ATtKTuJ0BGfY2uiPzGPWXbCVmq+ghuH98dicZdaW2nE7nq64nZdgJdCe4I7ORGTjkOWBb4fG4ghimqOiUotkoRGQKkNzIhzhMSAqPJpuX4D3D2kjGHUpuX53UsISN+1CjaP/csuG1nbDTXGLMnxgKTZ82axQcffOB1LCHluuuu05SUlHUiksP+H8k1Ychqcs2emACQcLh1WQhW9s03lM+aVV+b28HreIwxLYao6t/r6ur4+uuvvY4lpLRq1YrU1NT6rgqW4Jq9Ykmu2RMT1e+vSjr+OK/jCDn5/3qs/u2fW72OxRjTYowTkVE//PADBQUFXscSUoJKFfZ76zATvizJNXuiWBznvfhhw4hs29brWEJK+fTp9aO5F+HOvjXGmJ3xqepdNTU1OnXqVK9jCTm9e/dGVfMBG+I2e82SXLOnXgRIPrmx9oAHtvxHHq0fzf2H17EYY0LeYyLSZ/bs2VJaWup1LCElIyODzMxMRORd3JZZxuwVS3LNnvpc/f6NySefbDVSDZTPmEHJl1+C27vwYI/DMcaErjhV/TXA0KFDOemkk0hKStrVOQeMvn371n9qpQpmn1iSa/ZUrTjOy1E5ORI7ZIjXsYScvHvvQ2tqVFUfwr6/jDGNu0FEEsp+zMNfUcugQYO49tprOeKII4iJidn12WFMRBg8eLCq6lrcBRSM2Wv2S9jsjRcBUk4+2eMwQk/1ihUUvPKKiMhQ4Dyv4zHGhJzWqnpLTX65bnlzMevv/JbCT3Jx/MLo0aO57rrrGDFiBD6fz+s4PdG1a1eSkpJERJ7FShXMPrI+uWavqOosf3n5oCWjDhatqvI6nJDiJCbS5bNP1ZecvFEcpxtgBXfGmHrPABdvenE+lT8HdVRwIOWkrsQNzcbxOWzZsoUvvviC+fPncyD9nj7zzDPp2bOnBhZLsMV1zD6xkVyzV0TkJV98vCQeeYTXoYQcf0kJ+Q89LOI4rYCbvY7HGBMyBqjqRZXLCrdNcAH8UPjuUtb9dQYVCwtISU7m9NNP59JLL6Vjx46eBNvcEhIS6NGjByLyKZbgmiZgI7lmb2Wq6rqyadMiVl96mdexhB6fj07vvKPR3brWiOP0AFZ4HZIxxlM+VZ2OMizvXz9Ss65s5wenx5B+dk8i2yYgIixevJhJkyaRF8arTR5yyCEceeSRAKdjk85ME7CRXLO38kXk4/hRo4jIyvQ6ltBTV8fGu+4ScZwo4FHA1qQ35sB2nYgMK/1q7S4TXIC6zZXk/Ws2+U/Oobagku7du3PllVeGbSeGoAln+YCtb2yahCW5Zl+8KD4fSeNO9DqOkFQ+YwZF778PcAJwpsfhGGO801VV/167uUKLJ+3Zu/DVK0rYcO/3bH594XadGKKjo/dTuM2vY8eOpKWliYg8D1R7HY8JD1auYPZFtPr962pWr05ddsyxgv1f2o4vJYXOEz9WX3JygThOT2CT1zEZY5qVqOqXIjI2/6mfqFpetE8XSzgsh6TDc3AifZSXlzN16lRmzpxJXV3LbkRw2mmn0a9fP4AewGKPwzFhwkZyzb6oEsd5KqpDB0kYM8brWEJSXWEhG//6NxHHSQce8joeY0yzu1RExpZ+u36fE1yA0smrWXf7N5TOWE9MdAzHHnssV199NX379kWkZVZFxcbG0rt3bwWmYgmuaUKW5Jp99Ziq1qX+xlrC7kjxxx9T8sUXAL8Gjvc4HGNM82mnqv+sLarSoom5TXfV4E4Mi37pxHDJJZe0yE4MAwYMwOfzCfC017GY8GLlCqYpvA6cueyEcVQvXep1LCEpIiuLzh9/pE5c3DpxnN5AsdcxGWP2KwHeB07Y9Pw8Khdt2W83aumdGK666irNzMwsEpHWQKXX8ZjwYSO5pik8DJB23rlexxGyavPyyLvnXhHHaQvc7XU8xpj97izghLIf8/ZrgguNd2K44oorOPHEE0O+E0O7du3IysoSEXkZS3BNE7ORXNMURFW/1aqqYUsOHSv+on2vOwtX7V94nvgRIwB+BXzmcTjGmP0jU1V/9pfXpm28f6b4y2ub9eaxAzNJObELvrhIamtrmT59OtOmTaMqBFenPOOMM+jduzdAX2C+x+GYMGMjuaYpqIg87MTESOr48V7HEtLW33YbdaWlqn7/S4A1GDYm/AjwLxFJL5ywrNkTXICK2fmsv/Nbij5bgaPC6NGjue666xg+fDg+n6/Z49mR9PR0evXqBfAhluCa/cBGck1TiVK/f2VtXl720iOPEmqb/wd7S5F0wvG0/ec/wf3BfiJg34TGhI8rgX9XzNvE5ld+9joWcCDl5K7EDcnG8Tls2bKFSZMmsWDBArz+/T9u3DiGDBkCMBqY5mkwJizZSK5pKtXiOP+KbNVKEt1lGc0OFH/4EYXvTQB3kYjfehyOMabpDFXVh2sLKrXgrRDphOWHwnfcTgyViwpISU5h/PjxnndiSExMZODAgajq11iCa/YTG8k1TSlT/bqmYvaPUSvP+bXXsYQ0Jz6eTu++o5E5ObUiMgL4weuYjDH7JE1Vf6ROc/L+PVt2Z+leL0Skx5B2Ti8i28R72onhqKOO4uCDDwYYh/uuljFNzpJc09SeBS7KPX08lfPmeR1LSIvp05uOr7+u+Hy54jiDsLZixrRUDjABOGHL20so+36D1/HsUlSnZNLO6E5Eagx+v5/Zs2czefJkSkpK9vu9Y2JiuOGGGzQyMnKBiPQH/Pv9puaAZOUKpqk9DJB+2aVexxHyKucvYOO994o4TmfgKdwJK8aYlucm4ISyWRtbRIILUJ1bxIZ7vmfzfxeiVXUMHjyYa6+9lsMPP5zo6Oj9eu9hw4YRFRUlInIPluCa/chGcs3+8B5w0vKTTqZq0SKvYwl5bR95hKSjjwK4Ebjf43CMMXtmrKp+UbuxXPIemy1a0zJztsTDc0g8LAcn0kd5eTlTp05l5syZ1NXVNel9IiIiuP766zUuLm6NiHQBapr0BsYEsSTX7A+DgB+KP/uctdde63UsIc+Jj6fjG//VqM6dVUSOBz7xOiZjzG5praqztdqfmffoj1K7qcLrePaNAymndCVu8LadGObPb7ruXkOHDuWEE04AuA54pMkubEwjLMk1+8t7wEnLTz6FqoULvY4l5EW2b0+nt95UJyGhRBznIMCGwI0JbRGq+oWIjNn8n5+p+GmT1/E0GYmNIP2sHkR3S0UcYe3atXz++eesWLFin67rOA7XXHONpqSkbBGR9kBozs4zYcNqcs3+8heAzN9ah6zdUbNqFWt/d72gJKnf/z6Q7HVMxpid+ruIjCn5em1YJbgAWlHLpufns/H+mVSvLaVNmzZccMEFnHPOOWRm7v0aNr179yY1NVVE5BEswTXNwEZyzf70LnDy8lNOpernEGiK3gKknncerW67FWAibmudpi2IM8Y0hcuAJ6tWFZP/5E9QF96/R5uqE8MVV1xBdnZ2hYjkAJv3T7TG/MJGcs3+FBjNvcrrOFqMLS+/TOHbbwMcC/zD43CMMds7UVUfry2o0M0vLQj7BBeaphNDz549adWqFSLyFJbgmmZiI7lmf3sHOMVGc3efREbS/qUXiRs0COAS3N7DxhjvjVTVL/3ltdH5/54ttZsrvY7HE4lH5JA49pdODFOmTGHWrFk77MQgIlx11VWakZFRISKdgY3NG7E5UFmSa/a3AcDski++YM1vr/Y6lhbDl5FBx9df08i2bVVETsOdyGeM8U5PVf1Ga/wp+U/9JDVrSr2Ox1sOpJzSjbjBWTg+h4KCAr744otGOzEMHDiQk08+GeBvwJ+bOVJzALMk1zSHt4FTc089jcoFC7yOpcWI7NCBjq/9R30pKTXiOEcB//M6JmMOUG1UdTpK+80vzKdy8Rav4wkZTlwEaWf1ILprCuI4rF27ls8++4yVK1cCbl/ca665RpOSkgpFpBNQ5G3E5kBiSa5pDu5o7uTJrLnS6nP3REzv3nR4+SWV2NhScZzRwByvYzLmAJOsqlNFZEDBm4son5XndTwhKSI9hrRzehHZJh4RYdGiRUyaNIkuXbpwzDHHgC12YzxgSa5pLm8A41eefwHlM2Z4HUuLEjf8INo/84zi8+WL44wElnsdkzEHiGhV/VhEDi/6ZAUlU1Z7HU/Ii+qcTNr4Xzox1NXVERERsSEwintgFjEbz1iSa5pLJ/X7F1YtWRqZe8opgr9lLn3plcSjjqLtww8B5IrjjAI2eBuRMWHPAV4Fziqdvo7CCcu8jqdFiR2cReopXXEifQAvAhd4G5E5EFkLMdNccsVx7o/p0V1Sxp/udSwtTsnnn7PhjjsQx+mkqp8C6V7HZEwYE+BB4KzyeZsofN8S3D1VvbwIEVFVzcXtK2xMs7Mk1zSnf6jfvzHzd79TJyHB61hanMI33iTvgQcQkf7q908B9n7pIWPMjgjwCHBt1fJCCl5fBPaG5x5LPqYjEuGIiFwGVHsdjzkwWZJrmlOJOM4tEampknHVlV7H0iJtfupp8u6/H3GcvoFEN9vrmIwJIw7wOHB15bJCNj0/H2qttGpPReUkEjcwC+AjYJLH4ZgDmCW5prm9qKqz0s47TyM7dPA6lhZp89PPsPGeexDH6a1+/1SgjdcxGRMGHOAp4PLKJVvY/MJ8tMYS3L2RfEJnVLUO+IPXsZgDmyW5prn5ReR3Ehkp2TfZz7+9VfD8C2z4+98Rx+kRSHTbeR2TMS1YBPAccHHlogI2vbjAEty9FNsvg+gOSYjIE4Atc2k8ZUmu8cI04I3EI44gbuRIr2Npsba8/Arr7/gL4jhd1e//H2BD48bsuWjcFofnV/y8mU0vL7AShb0VISQf10lVtRj4i9fhGGNJrvHKTer3V2XfcrPi83kdS4tV+PrrrP/Tn8Ft0fYV0MvjkIxpSRKAD4BTymfnsfmVn6HWZpntrcQx7YhIjRER+SuQ73U8xliSa7yyUhznvpju3SVl/HivY2nRCt96i3U33wJ+f476/dOBMV7HZEwLkKqqnwNHlX67noL/LoI6S3D3VkRWHEmHt1dV/Rl41Ot4jAFbDMJ4K0H9/sX+0tJWy447Xuo2bfI6nhYt/uBRtHvkEZXY2FpxnN8Ar3sdkzEhKkdVPxKRfsWTV1P86Qqv42nZBDKvGEBU+0QVkZGALWtpQoKN5BovlYrjXO1LSpJWf7rN61havLKvv2HFOb+W2k2bIoDXgJtwe34aY34xSlVniUi/oom5luA2gYRRbeonmz2EJbgmhFiSa7z2DvB20jHHkHjUUV7H0uJVLVrEijPOlMrFSxS4B3gMd+a4MQYuUtWpWuPP2PTyAkqmrvE6nhbPlxZD0q861q9s9mev4zEmmJUrmFDQSv3+hXUFBUnLjjte/MXFXsfT4jkJCbR75BHiR40Ed2LNOUCpt1EZ45kI4D7gd7VbKnXzSwukZn2Z1zGFhYyL+xLTLRXgCOBLj8MxZhs2kmtCwQZxnN9FZGRI9k03eR1LWPCXlrLq8sspfO89gHHq938H9PA2KmM8kQp8DPyuankRef+abQluE4kbml2f4D6NJbgmBNlIrgkVAnwKHLXywosonz7d63jCRtpFF5H1+9+DUBqYkPau1zEZ00x6qeoHItKldMZ6Ct9fZh0UmoiTGEWr3w9RifZtEJHeQKHXMRnTkCW5JpR0VL9/fs369bHLTxgnWlHhdTxhI274cNo++IBGpKUJcDfwJ6DO47CM2Z+OU9XXURIL319G2bfrt3lyfUk+90x9ii+WTUfVz7heh3Pb2KtIiI7b5YWv/+gu3pr3SaPP3X/cLZzR71i2VBRz0yf3MH3VbABGth/I3466nuyEDADumPQIb8//jLuOvoFxvQ4H4PWfPuLUPkcT5Yvch5fdPNLP60VsnwyAk4D3PQ7HmEZZkmtCzbXAw5tfeJG8u+/2OpawEtGqFe0eeZjY/v0BJuHW6VrDdhNuBLhRVe/Rilo2v/qzVC0r2uaA8uoKfvXCRWTGp3P9wRdQUlXKX7/8N62Tsnj7nEcR2XlTktVF6yko3/aaSzev5PqP7mLiBc/QJ7sbV064nY2lm7nh4AspqS7j/q+eJToimo/Of4r1Jfkc9dwF/GnslTzx3etMufQVqutq+Nvkf3Pnkdc19dejycX2yyD9173AbVN4tsfhGLNDNuvahJrHVPWstN+cN7J44kQq58zxOp6wUbthAyt/fS7Zt91K6llnHal+/w/iOKcC33sdmzFNpDXwHHBMbV65bnpxgdQVVG530LsLPmd9ST4Tzn2ctLgUAJKiEzn7v9cza+08hrbrt9Ob5CS3Jie59Tb7nvzudY7vMZY+2d2orqth4qL/8e65jzGoTW8AEqLiOOe/N7CueCPrivPolt6BU/v+its+fxCAD37+knE9D9/nL8D+5sRFkHJyV1XVLSIS+hm5OaDZxDMTaupE5BJEatr8/W8qkaH/tl1LojU1bLjjL6y79Va0tratqn4D3ALY2sqmpTtFVecBx5TN3EDeY3MaTXABftqwiD5Z3bYmuABd0tsDsLp4wx7feFF+LhMXT+WGQy4EoKiyhDrdthqopq4GgJiIaPwoIg6OCH71A/D9mp8YtovkOhQkn9AZX3ykiMi1QJ7X8RizM5bkmlC0QETujO7aVTKuvdbrWMJS0TvvsuKMM6V62TIfcJeqTgE6eRyWMXsjEXf09h1/RW3qppcXsOWtJWj1jkvOHXHYUrFtucGi/FwAWiVk7nEAT33/Xw7rPIJuGR0ByIxPo0dGJ+776hnyywpYW7yRh795ibGdhpMWl0JWfDpritazKD+XrPh0vl8zd5ejx6Egpkcq8YOzAT4C/uNxOMbskiW5JlTdo6oz0i++iLgRI7yOJSxVLVxI7mmnS8GLLyEih6jf/xNwAbZKmmk5DlbVn4ALKxcVsPHBWVI5f/MuTxqRM5DcLWt46rv/ApBXupm/T/k36XEpDG3bd48C2FJRxISfJ3HB4FO32f/kyX9l1tr5DP7XyYx4fDxFlSU8euL/AdAxtS1d0tpzzAsXc+6gk/hg4Zec2OuIPbpvc5NoHymndFNVLQWuBGxCjwl5NvHMhLIu6vfPqd20KS73xJOkrrDQ63jCVvyoUbS++x8amZUluC3GLgM2eRyWMTsSCdyuqrdQq1L48XIpm75+lyfVq/XXcs0Hf+XDhZNJik6grLqCOq3j2pG/4Q9jLtmjQJ6Y8Rr/mfMBUy99deuEtZq6Wk7/zzX4xOHcQSdRUlXGv799lXbJrfjPmQ8QHRFFnb+OjaWbEIQ35k0kyhfJY9Nf4eqR53HF8NCby5U6vjvxQ7LBTXCf8DgcY3aLjeSaULZMHOe3kVlZ0vqvf/U6lrBW9s03LB93ohR/PBHgFPX75wEneByWMY3pqarTgdtq1pU5Gx/5YY8SXIAIJ4LHT/oLH/zmSW4/4hp6ZnYmKTqBSw86c4+DeXfB54zrefg2HRkmLf2aVYXrePXM+zm1z9GcP/gUXj/rQb5fM5dPl3wFgM/x0SYpm//M+ZDxfY/hoa9f5P7jb+Ghb16g1l+7x3HsT3HDsusT3I+BpzwOx5jdZkmuCXUvAa8nHnUkKWeM9zqWsOYvKmLtDTew9g834S8vz8JdDvhNoK3HoRkDbjeg36nqjyhDir9cRd6/Z1Obv/f9tAe27sXoDkNYsnkFVww/m5SYxD06f9nmVSzIW8pxPQ7dZv/yLatpn9KG2MiYrfs6peUQHxXLqsJfEvKKmipKqkoRcUiKTuBX3UaTEBXPprLCvX5NTS2ydTypJ3VVVV0FnAf4vY7JmN1lSa4JdQpcqX7/quxbb9Xobt28jifsFX/wAcuPP0GKP/0U4HT1+xfi9i+2DgzGK2NV9UfgwbotVdH5T/5E8Wcrm2T1sie/e53U2GQuGbrnf0RPXPw/Widm0id7259LabEpLN28krLq8q37ZqyeQ2l1Oa0SM7bue3f+Z5zS52gANARLXCXGR/q5vRSf1InI6UCB1zEZsycsyTUtQaE4zlkSHe1v+9CDKrGxXscT9mo3bmTtdb9j9eVXULthQzzwsKrOAIZ6HZs5oOQA/wUmU6d9iietZOODs6R6ZXGTXHxt8UZemf0+fxh9yTajrlsqipizfiF1/p0vCjht5UyGteu/3f4xnYZRXVfDSS9fyV1THuePn9zHhW/dTHZCBsd0G7P1uJ82LGRA655kxKVSUlXGx4umUFpdRkZ8SpO8vn2VNr47EemxEuiHa/20TYtjSa5pKaaLyK3RXbpIqz//yetYDhilU6ey7PgTZNNTT0Nd3ZBAovsIkOx1bCasxQC3qeoi4IyK+ZvY8MAsKZ60Cq1punfL//nVs3RJb8/4fsdss//zpd9wwkuXURo0EttQVW01M9fOa7QbQ9ukbN485xFSYpN4ftY7vD3/U3pmdeHZU+/aumzw/I1LOKSj+zdjpC+C60b9hj9MvJffjbqACMf7dZoSRretX7b3NeBxj8MxZq9YdwXTkjjAh8Cx6/54M0UTJngdzwEluns3Wt1xB3GDB6N+f544zp9w+5PufLjLmD1zgqo+LCKdazZVaOH7y6Rq8RavYzqgRHVIIvPy/iAsEpGhQKnXMRmzNyzJNS1Npvr9c7SqqlXuaadL9fLlXsdzYBEh+dRTybrheo1ITxdVnScidwOveh2aafG6AQ8Bx/mr67Tki1VSMm1tk9Tdmt3nJESSfd1gdRIiK0VkGDDf65iM2VuW5JqWaIyqflmzapWTO/4M8Rc3TX2e2X1OfBzpl1xK2kUXqkRFiYisAU4GZnkcmml5MoCbVPV3IhJZ/mMehRNz8RdXex3XgUcg4+J+xHRNATgX++PVtHCW5JqW6lrg4dKvvmL1FVdCnb1j7oVOH7xPzLYdL94E/gQs9iYi04KkATcEktv46vVlWvj+UqnOtT9avZJ0VAeSjmgP7mIPV3ocjjH7zCaemZbqUeDZhNGjybrx917HckCKGzGC6K5d2ZBbxGt3ziB3Tj7AeFVdADwP9PQ2QhOicoB7VXUFcFttfkXc5v/8TN4jP1iC66GY7qkkHdEeVZ0FXO91PMY0BRvJNS1ZtKp+ISIH20S05td18pdEtm7NO//8gfVLCwFo1TmJ4Sd1oV2PVFRVReQ94B9Y+yEDWcCfVPVqEZGaTRVaPGmlVMzJJwRbxB5QfMnRZF83SCU2olhEBgG5XsdkTFOwJNe0dNnq98+krq7tinPPk8o5c7yO54CQfOqptLnr7+TOyefjx+du93x2pySGHNOBTgMy63d9iZvsfoGlNAea9sCNqnqpiMSoKiLC+nu/p66g0uvYjE/IumIAUTmJACfirnRoTFiwJNeEg8Hq939dV1AQnXvqaVKbl+d1PGGv+3czkIREXv/rDLas33Ev0bQ28Qw6uj3dD2qF4wiqOivQjeFdrPVYuOsF/FFVfy0iERuXL2XGe2+S2roNo88+n9Lp6yicsMzrGA94Kad0JWF4a4B7gJs9DseYJmVJrgkXZwKvV8ydy8pzz0OrqryOJ2xlXHsNmVddxfyv1jLl1UW7dU5iegwDj8yh9yFtNCLSJ6qaKyKP49bubtqvAZvmFAecBlwMHAqwev5cZrz3Bit/+nHrQde88AaRkTFsuPd76qyLgmcSD88h+eiOAJOAY4FaTwMypolZkmvCyd+A24ref591N/3R61jCU1QUPb7/jjqJ4JU/T6e8aM8SlNjESPqNbUffMW01NjFKVLVaRN7Anc39DVbK0BIJMAy4SP3+c8RxEmtranTp99Plh4/fZ/2ShdudMOLUszj4zHMp+XotRR9Yr2svxA3NJu307qjqbBE5FLBZfybsWJJrwomD+zb4iRvv+ycFzz7rdTxhp/U//kHKKSfz/Ue5fPfB3s9NcSKELgOz6HtoW9p0SwFAVReKyLPAS4DVnIS+TOBcVb1IRPoC5K1YxtwvP2fhtClUlu18kaxrXnyLSF8U6+/9Dn9JTXPEawJieqSSfn4fEFaKyAhgg9cxGbM/WJJrwk2S+v3Tgd5rf38jJRMneh1P2HBSU+n21VdUVdTx8p+nU1PZNCW1aW3i6X1wG3qMaKUx8ZGiqrUi8j7wOvAxUNYkNzJNwQccDVysqieKSGRVWZku+GqyzJv8GXkrdn9U9uAzzmXEaWdR8tUaij6yyfzNJSonkczL+ikRzhYRGYn1tDZhzJJcE446qt//DXV1rVZffoWUffON1/GEhZxnnyXh4FFMfW0R86aubfLr+yIcOg3IoNfBbcjplYqIoKqVIvIh7iITHwM7Hx40+0sP4Dz1+y8Ux2kDsPKnH5k7+XOWfj+dupq9G4m99qW3iXAiWH/39/jLbDR3f4vIiCXzygHqxEVUichYYIbHIRmzX1mSa8JVX/X7p2lVVdLK834jlfPmeR1PixbVpTOdP/iQ4k0V/OeOGfj9+/fnRnxKFF0GZdFlcBatuyYHJ7wf4ya8H2IJ7/4UBxwGHKuqx4hIF4DiTfk6b/LnMn/qJIrz972iZPQ5F3DQSadTMmU1RZ+s2OfrmR1zEiLJumqg+lKj/SJyMu73kDFhzZJcE84OVr9/kr+4OHrF2edIda69Jbq3Or33LjE9e/LJU3NZ9kN+s947LtlNeLsO2SbhrRKRT4H6zXpR7RsBuuPOsD9WVceKSBRAcX6eLv9xpiz9fjqr5s5B1d90d3UcrnvxLRx8bLj7O/zlNrl/f5AoH5mX9yeqbQLApcAzHodkTLOwJNeEu+NVdULtho3OirPOktqNG72Op8WJGzGC9s8/x8YVxbx9zyxvY0mKovOgzEDCm4LjCACqulpECnF/ef8Ha0u2O+pHa49T1eNEpCNAXW2NrlkwT3JnzyJ39kwK1q7Zr0GM/c2lDDn+JIq/XEXxZyv3670OSD4h4/w+xHRPBbgduNPjiIxpNpbkmgPBecBLVcuW6cpfnyt1hYVex9Oi/LJ87yzWLy3yOpytomIjaNczlfa90+h+UDaR0RFbn1PVuSIyBZgC/A9LegHSgIHAYOCo4NHaoryNmjt7puT+OIvV83+ipqr5ViJznAiueelNHL/D+ru/QytsNLfJCKSO70784GyAp4ArsDZ95gBiSa45UFwPPFA+ezarLrwIrajwOp4WYVfL94aKSx4YjePUMeXlZ2jXqy85ffprQmqa1D+vqotEZC4wL2hbRng2vxcgBxiEm9QOUtXBIpJTf0BdbY2unj9XcmfPYsXsWRSs27+jtbty+EVXMOhXJ1A8aSXFk1Z5Gks4ST6mI4ljcwDex12kIxz/vxuzQ5bkmgPJP4CbS//3P1Zf9VuotZ/3u7J1+d47Z7Blw46X7/VSbGIkF957CLk/zuTde/6ydX9q6za0692P9n36k925q6ZktxZxnK3Pq2o1MF9EghPfecBqWs5oVwRu54OBuEntIFX/IBEntf4Af10dm9euJm/FcvJyl5G3Yjkbli2mNoRWBXR8EVz74ptInbijuU3Unu5AljCqDSkndkFVvxGRo4DQ/AY2Zj+K2PUhxoSNW4GshDFjLmrzj7tY98ebwd+Ek2jCTMa11+BLSmL+V2tDNsEF6DO6DSLCyrmzt9m/Zf06tqxfx9wvPgWQiKho0tq2IyOnQ/0WldG+48DE9IxBweep318ijjMXWACsB/KDtk1BH/f3erQObolBFtAOd3S2feBjjqrmAB1FJLr+hJqqKs1fsVzyViwjb0UueSuWsWn1yr1u8dVc/HW1zJs6iQFHHkvCqDaUfLna65BatNh+GSSP61z/DsY4LME1ByhLcs2BRIHLgbTkceNO1jo/62+91RLdxkRFkX7xxdRU1/Hdh6HdlaLTgEwAVjVIchuqra5yRzJzt2nEINFx8aTntA8kvh3JyOmQmNmh48iYhMRRO7ue+v3FiOSJSH3iW7+V4pYM+HAT1ca2hs/5gBQgS1UzUc1GJE1EfI3du6ayUos350txfh75K3PJW7Gc/BXL2bJ+nTRp94NmNPmFp+g79igSR7ej9Ot1aJWN5u6N2H4ZpJ3dE5QN4sivgAKvYzLGK5bkmgNNLXAW8GbKySeNkwifO6JbZ79Qg7W+8y840dHM+iiX8qL9PWC5b9Jax1NWVMim1Xs3M7+qvIx1i35m3aKfg3dLbGIScckpxCYlE5eURGxSMrGJScQlJQc+T06KS0pKiktK7hKblCSOb99/nFaVlWlZUaFUFBdSXlxMRXER5cWFlBZspnhTPiWbN1GyOZ+qsjLZ9dValrqaGn6eNoW+Y48kYWRrSqZ4WyfcEsX2zyTtrB4AG8SRsYC1qzAHNEtyzYGoCjgdeD35hBNOkYgI1t74B6vRDXBSU0k6YRwVJdX8+FloTwJKaxNPRJSPxTN+bPJrV5QUU1FSvDuHCkB0XDyxSclERkejqqjfj6of9av7WP3uPn/g8/pj/O7n1RXl+Ovqwi553RNfPPsEvQ85jITR7Sj9Zh1a3TJHpb0QNzCT1DN6gLAusJrZEo9DMsZzluSaA1U1cCbwatIxx4zH8bH297+HEK9dbA5t//lPnAgf3324lJoQf8u4z+i2wK5LFZpDVXkZVeVlXofRotVWV7Jw+v/oPfow4oe3pvSrpl8+OhzFDc4idXx3gDWBBNcWRzEGS3LNga0GOAeoSTr6qHPkkYdZe+116AGc6EZ16Uz8yJEU5pWz4Kt1LFr7IzOXfkFVTQVdW/fn4F4n4HMaLRPdzvdLvmDirJfYUppHakIWRw08m5E9j9n6fE1tNW9P/zc/LJtCbFQCJw2/hMFdxgJQVVPBE5/cxpbSPC4/5m+0Tu0IwFfz32d0nxO3XqN97zQAVs2b0zRfAOO5L555jJ6jxpB4aDvKvl2P1tho7s7EDc0m9bRuAKsCCW5oF9Eb04ycXR9iTFirBX4DvJR42GG0e+xfSHT0rs4JW23vvx9xhOnvLuO7xV/wxMRbERyS49KZMONpXply725dZ+3mZfxn6j8Z0/dkfnvcPQzqfCivTr2PuSunbz3mtf89wKylkzlt1FUcO+RcXplyH7kbFwDw04pvUFX6th/JlLnvbr2mNujslZQRQ8G6NZRstrUewkV1ZSVLvvsGX0IU8Qe18jqckBY/vBVpp3cHWCEiY7AE15htWJJrDNQBFwLPJowZQ7t//xuJifE6pmYXN2IE0T16sGF5EYu+X8tbX/+LMw65lnMOvYHTRl3FmYdcx8ylX1JRveu35H9c/j+6tRnI2L6n0LVNf8YddBGdW/Xlp9xpAGwsXM33SyZx9pjrGd79aEb0OIbD+5/OJz+8AkBByQY6t+pLn/bD2VyyAYAZiz9jePejt94jp3cajs9h5U9NX49rvPXZU4/ir6tzFzKIsF9TjUk8tB2pp3RDVZcHElybZGZMA/bTwxiXH7gMeCLh4FHkPPkEEhfndUzNqs0/7kJE+OadpdTUVXHCsAu3SSpT4jNQ9VPn3/UEvdLKIhqup1BXV0NkhDtKvnjtj0RGRDOg4yFbnx/Q8WAWr5uN31+HojjiICKo+imtKCLSF010ZOzW43uNag2wXX9c0/JVl5ezbNYMfIlRxA/L9jqckJN0TEeSj+2Eqs4TkUNwFzAxxjRgSa4xv/ADVwGPxg8fTofnn8OXmrqrc8JC8qmnEtm6Nctn57N+aRFx0Ykc0nscTqD+tqaumslz36FLq74kxCTv8no92g5i4ZpZzMmdRlVNBd8u+pRV+YsZ1u1IAArLNpGdkoMvqO1WelIbamqrKCzbRFJcGnmFa8grWkNyXDrfLPyYkT2P3eYebbqm4Pf7WT0/dJcbNnvvsycfxV/nJ2lsDvgO6KYTvxBIObkrSWNzUNXpgRHc9V6HZUyosolnxmxLgeuA0tgBA27p+N/XddWll0nNyvB+JzD75j/i9yvfvrf9pOyJs17im4UTifBFct24B3breoM6H8qCHt/z9Ge3b913xiHX0im7NwA1dVXERiVsc079KG1pZRF924/go+9f4KeVX3P5r/7G3JXfkJHUeuuxToRDXFIk65cuprrCFnMKR5WlJeTO/p4uQ4YTPzSbshkbvA7JWz4h7YwexLmLn3wuIqcA1s7DmJ2wkVxjtqe4SwBfGZmTox1ff01jBw70OKT9J+O6a/ElJfHztHWNLt/bPrMHnVv1paBkIzOXfLFb1/xpxdfMWjqZYwefx8VH/R/Dux/N29/8m59WfA1AhBOJ42z746d+rK6mtoqkuDT+76wX+euvX6esqpj+HQ/m6c/u4JaXTmfx2h/pcVA24jhWqhDmPnviUdTvJ/GwA3s0VyId0s/rXZ/gvgOMwxJcY3bJklxjduwJETnJl5xc2f7FFzTxyCO9jqfpRUWRftHOl+/t0344Fx5xGycPv4wJM55mS2neLi/74ffPc/zQ8zl+2AUM6nwo5x32RwZ3GcuEGc8AkBibSmFp/jbnlFW5Cy9ERbqT/qIjY0mMTWHJutlE+CLZWLiKIwecwWezX6NboE5z5VybdBbOyosLWfHTj0SkxBA3OMvrcDzhJESScXE/YnumATyP29+7ytuojGkZLMk1Zuc+FMcZI5GRm9o+8jCp553rdTxNyl2+N4rZn6+ivPiX5Xvr6mopKNm4zbH9Oo5CUTYW7nqOS17RGlqnddpmX7v0LmwudssH26Z3Jq94LSUVW7Y+vyp/MQDJcelb963IW0iHrJ5sKc0jJ70r/TqOYnPJBrI6JFJTVcX6xYv2/EWbFuWTxx9C/X6SDmsPzoE1mhvZJp6sqwdpdMckgPuAS3DbHhpjdoMlucbs2kxxnOHA4la33UbWzX8Eafm/bJ3UVJLGNb58b+7GBdz53/O3GbXNL3JXn0pP3HXv0oSYZFblb5uALlj9PcnxGQB0adWP+OgkvpjzBgCqytR579E6rRNJcWlbz5m5ZBLDuroj6PW9GhxHiIqNYPWCufjr7Pd9uCsv3MKq+T8RkRZD3MBMr8NpNrH9Msi6coD6kqNqgQuAm3AnxxpjdpNNPDNm9+SK44xU1QnpF1xwSGTr1qy76Y9oVct917Dt/f/E8TW+fG/n1n1pk9aZf398CycMuxAR4Z3pj9O3w0gyk9tSWLaJ0opC2mV0bfTa/TqM4pNZr2ztjrBs/VyWb5zPKSOvAMDni+CUEZfz8uR72LBlFRXVpSzbMI9Lj/7L1msUlW0mNjqRyIgo0hKyWb1pCXNyp9GhfQdEJCSW8jXN49PHH+LSfz1P4uHtKZ+dF96pnkDSEe1JOrIDqpovIicD03d1mgldIpIBbFZV3eXBpklZkmvM7isQkaOAF5N+9aszIjIzWXPVb6krLPQ6rj0W1aUz8SN+Wb63IUccLv/VX3ln+uO8OvWfRPiiGNJlLCcMuxCAr3/+iClz3+a+C99v9PqnjrqS+Jgkpi+cSEllIQkxKRw18CwO63vq1mMO6n4UcdGJTAqM5l72qzvp3/Hgrc//tOJrRvU8DoBO2b1pldqBL396ixeefQnAFoE4gJRs3sSahfPI6d2P2P6ZVMzO3/VJLZBEOaSd0YPYvhmo6qxAgrvG67gARCQS6KuqPwbtiwOScWuEd/dPDweIAdbvLOkTkVigWlXrAo8jVHWHb92ISFsgcjdjAFjZ8P4iksTuv8Ndo6q7nPwnIlnACuBsYMIexGeagNgfFsbsMQe4G/hD9Zo1uuaaa6Xq55+9jmmPdJrwHjE9ejDxybks/7FlJQyXPXIoNRUlPH5Z6NZHL9qQzzuz5nHL8Ydt91xReSUPTZrGNUeMIi1+7xccKSyv4L5P/seFhwyla5Zbx1xVU8uE2fOZu2YD1XV+ctKSOWNof7KS3HZtE36cz6yVazl1SF8G5rQB4LvlqxncoS0RvtCuXkvOzObiR56hdlMFGx+c1XCtkRbPlxpNxvl9NLJVvACvAxcDzdIfT0T6Au2BWCAOSAUyAltboDPQHYgCfqOqLwfO+zXuZLgq3JUjAaJxk83SwOOkwOuoT1CdwH0yVbUwKIY+QFfgI1WtFZEzgEeAYUAx8C1wpapO2cFrmAf02YOXHaeqFQ2usRTospvnf6qqx+zOgSLyAZCoqmP3ID7TBEL7p5oxocmPWx93WWSbNrUdX39Nk08+yeuYdlvcyJFEd+/OhuVFLS7BTW0dR2SUL6Rbh20oKuHVb3/E38gAQmVNDS9+M4uSyn0vc3lz5lyqarcd2Hpr1lzmrd3I2B5dOGVQHwrLK3hu2kz8qhSVVzJz5VqO79+TT+e5k/xq6/ysKywO+QQXoCh/I2sXLyAyK47Yfhleh9Okojolk3X1II3IjgO3feE5NFOCG9AfuAe4BjgZGAAcAvwa+Ay4HTgMNwF8u/4kVX1VVaNUNVFVU1Q1Bfg7MC3ocR5wQv1jVU1S1cjgBDfgeOBJfslLegFbVHW1qhYBk4B3RKT7Dl5DNXCpqsrONqBf4PjGvglrgGsaHL8ZuLDBvhd3cP6OPAhMEAmDyRwtTOj/ZDMmdD0tjnOIREaua3P33bT6v/9DIvfk3TJvtLnr7+7yvW8v9TqUPdZ3TFsgdJfyXbW5kMcmTyc9YfsR2rKqah77cjraBEOQ3+WuZuXmLdvsyysuZc7q9Vw65iCO6N2VEV3aM35ofzaVlrG+sJgt5eVkJyYwpEM7CsrcAaw5q9cxoH3rxm4Rkj574hHUryQd0f6XxsotXPxBrci8pB9OXER5oDzhHzTjOLWI9MQdJR2HO8Ht98BfgS9wE+2PgB+Adbh/4GeJSLdAeQAikiAiWr8Fzj006HE2MDnomBU7COVQ4BlVrW/zMgz4NOj5G4AlwPU7OL8WeDo4lsY2oH6JxJgdXGN31TW2U0TeaOSeXwAPAP5GYmq3B/c0e8iSXGP2zXfiOINU9cvUc86mwysvE5Gd7XVMO5R82mm/LN+7rMjrcPZY+97u2/KhOulsef5mxg3oxaguHbZ7bkNRCV2y0jln+KB9ukdRRSUfzF7AuAG9ttmfHBfD7446hJy0lK374qLcP7pU3U3E3erL1HI3baFTRhotxZb1a1m/bDGR2fHE9knf9QmhzBFSTuxC6qndwCFXRIYDjRe571/TgTnA7AbbbbgJasP99dtfA+fXj2iOwy1z+DvwdeDzVNyR3PrnbsAdcd2GiAwCjgRmiEjPQOI9HFgR9LgLcCPwLxHpH5jMFcwPXBt039eAfwU9LgiKI1VVGxsp35MpjTs6tgZ4D3ckemfbrYHjW+7s5RbAklxj9l2+iPwKuDd2wAA6vfOOxh00zOuYGpX9R3f53unvbr98b0uQmBFDwbo1lGze5HUojRrTozMHdcpp9LlOmWmcPKgPvn3s9fr2rLl0SE9leOf22+yPjoigTUrSNvsWrs8nOsJHq+REEmOjKSirYENRCUmx0eRuKqBjRuo+xeKFTx9/CFUlsQWP5jpxEWRc3JeEUW1Q1ckiMgyY70UsqpqqqolAO6Bj0HYbsL7Bvo5AlqrGq+pFgfNrcEeeSwMlCJVAraoWBh4HP1eBmwQ2dAVuve/buKPK3+PWA/8JmBa0vQv8DzcxP77BNfyB61cBRbjJdFXDOIKea/TLATzaYBQ2HXi+wb7zd3A+gddXpKoLd7YFvrawgxFh0zQsyTWmadQCfwRO96WmlLd//nnSLrrQ65i24S7fm8jP09ZRuLE5y/2aRk7vNHw+h5U/zfY6lB1ydlJyt7PndtcPK9eSm1/A+KH9d3lseXUNXy3JZVTXjkT4HDIS4slKjOfBz6cxsnMH5qxav3XyWUtSsHY1G5cvJap1AjG9Ws4odL2I7Diyrh6kMV1SwB2V/BVu3afX3gO2BG0PAG0a7NtCgwRPRCLYsz83Gqvpug13IlpUoI73aeBLVc1oZEsPJNkvNrhGfbKYi5vwng/8vkGyOjnw3PIdxCbsfk3ujuxp0hrODfE8Zy3EjGlab4vjzFe//73sm27qEduvH+v/9Cf8ZR4nlbuxfG+o6zXSrR0N1VKF/a2ksor3fpzPSYP6kBzXWDnhtt79YR4+x+Gwnr9MFr90zHCKKysBmLliDV8tyeXLn5dyRK+ujO25u5PKvffZEw9z3r2PknREeyoXFHgdzu4RSBjZhuRjOykRUgf8FnjK67CCVOJ2M7gt8Phi3Am2PYKOmc/2b6/X/2ecHDyvKpBY0shzKxveWFW3vjUjIvHAhcD1IpLSSJwamIjWUH2yODAQ42O4CeqfA/uX4Sa+X+N2gGhMUwz8+YDkQInFztQXw7fQ9yNaBhvJNabpLRTHGQa8lXTssXR8802N7tVrlyftT63vvNNdvvezbZfvbUnadEvB7/ezav5PXofiiXd+mEenjDSGdtz1PJUfV61l9qp1nDGs/9a6XHBXi0uJi2XG8tUM7diOzxcs4cyDBvD5giXU+VvOgFL+qhXkr8wlqm0iMT1Cv+TCSYoi48K+pJzYBSJkhYiMIbQSXHBHIK8FSgLbQ7gjuSVBW3uCRh4DfXIrGoxyxgEfA88ATiPdDTqJSHwgmW3MrUAKbmuyhqPIW3DbqzVGcEdrY3B790bhJrMpgU2AhMBzMSLSPRB/sAggVkRS6rfAeXEN9kXt6IsIpOF2qPh5F9tdQfc0+4klucbsHyXAGcCNUZ061XV647+afskl4DT/t5y7fO8J7vK9n6/a9QkhyIlwiE2KZMPSxVRXtLxSi6Ywd80G5q/byI1vfLR1A3hiyrf8e/IvC2JtKCrhzZlzGdOjM71aZ213nZraOiprahCE2MhI+rZtRXRkBKVVLeuPn0+feARVJenI9rs+2EOx/TJodf0QjemeCvCsiAwgNFcw8+G28MoJbLcAG4Ie5+D2qw3+IbYAqG1Qr1oOHAdcQiPdBHCT5FLclmXbEJH+uJPLSoG7GyTHbXHraW9teF5AEm6rs9nAQuBE3J/BiwKbAzzBtpPnGs4CjQHuZdukOg13VDh439k7iAEgE3huN1qZHYzbdSHMOj6HFvsLwpj9R4H7RWSy+nyvZt34+54Jhx7Kupv/SM3a7VcZ21/a3n//DpfvbSl6HJSN4zgh2zqsOVx/1CHb7Xvw82mMH9qPLplup4Hiikqe/ep72qYkc1y/HtsdDzBr5VoGd3BbsTVFOzOv5OUuZdPqlWS270h0txSqlhR6HdI2JNpHyoldiB+SjaoW4CZ973kc1s7EAJcHtmCrGzmu3iG4E63qSxiuwk2OjwXmBfYdDvwHN6lbxi+LQVQGX1REUvll4tnvgf+JyLuq+l3gkEeBicErrjXQBjhbVSeIyN1AK1W9QEQuBy5T1SGB+5wJRKrqK41cIwM4R1VfC4prE3Cjqr4QtO8F3FHhbYiID+gLvLWDGLdS1W9wO0qY/chGco3Z/34QxxkMPBo3bCid3n+/2RaPcJfvHbHD5Xtbiq7D3LZs4VyPW1lTy+qCQmpqG/9DpG1q8nYbQHpCPBmJ7ju/r303h9Kqag7r2Zl1hcWsLihkdUEhZUGjtGu2FJGTlkJCTBSVNbX8tGY9VTW1JETv7B3Y0PTZk4+6o7lHhNZoblTHJLJ/N1jjh2QDfBxYUew9b6PaMRGJxu1W4AsaabwSWNtg9DESeCUw2QxVXaOqGwPdC8YBf8MdNe2JWxaQgJuc3q6qswKdDgpUda2qbg66fypuT1wfcIaqzgTuBN4TkfYickfgmlfsIP62uCOoSxp5+r9AVxE5VUS64I7mdtrBNRJwSwn21tjANabtwzVME7KRXGOaRwVuvdtHTmzsC23uvrtVwmGHseH2O6grLNxvN237wAOII0x/dxl+f8sdtcvukEhNVRXrFi/0OpT9Zs2WIp6Y8i3XH3XI1gR2T5RXVbNkozt/57lpM7d57sxh/RnWKYe1W4rolu22F/U5Dkf26sYb3//EUb274fOglGZfbVi6iIK1a0jvmEN0l2SqvO797BOSjupA4qHtwB3dvB63BCDUv/lux421RkTqa26jcGtXC4OOi8Ctcz2LoJXPAj7GTXRH4r6d/zDu6y4HvhIRR1W3K/wWkaG4I73VwGGquhFAVe8WkX647cQAhu9gwhnAYNxSikUNru3DLXO4DXgJt23XA6r61+2u4K7oVgTsS9H/73GT5O92daBpHqKNLD1pjNmv0nFHE06vzc/XdbfcKmXTmv4P/7iRI2n/3LNszC3m7XtnNfn1m0tMQgQX3Tea3NmzePfuO7wOx4SYtj37cOYdd1OdW0T+U3N3fcJ+EpEVR9pZPYhqk4Cqfi8i5wKLPQtoN4lIG9xktJJtk/ELcetj+zQ4pb7coCJQhhF8rRjcEoZzgdNxJ9e1xh0lrgImAm8Cn9avbCYi/weMwh3BLQ7s8wEn4U7OygnEdjvwvKqWNfIansHt33ti4PFDuCumxQB5qnqsiDwIXIebfP89uKND4JzPgfWq+psG+wtwyxWeC9r3Dm4P3rOD9o0H3gAuUtXnG8ZovNHy/nQ3puXbjDsh4je+9PTS9s88Tfaf/4TE7Lot1J5o84+7WuzyvcH6jG6LiIR1qYLZe2sXzqdwwzqiO6cQ1WnPR8D3mUDCwW3IvnaQRraO9wN3isjBtIAEN2Aibq3sMmBp0HYb7qjt0gbbEtyJXX8SkTYiMklEvhWRhbijqc8BG4HeqnpDIBHMxK1JzsAt2/hSRCIBVPVO3IlqsSIyTkQeBlbgDgS8EDj3NtyFITaIyPsicktgAY36Uoez2LbrwljcxDkXtxUaqno9cA5wCrBGRCYHXWMUbn1sY8lpVGBDRAYEkt5TgG/qDwgk938BZuCOGJsQYSO5xnirg6q+JCJjqles1PW33y7lM2bs80WTTzuNNn//G8tn5zPxCe9Gt5rC+JuHktUxiRf/cDWbVq3wOhwTgnL69Gf8n/9O1dJCNj07b9cnNBEnKYq007sT0z0VVV0eGL0Nxc4J+42I/A53qdwlwCxgoe4ksRCRTkCNqq5psP8I4AN+Ge2doKoVQc9H4yaXJwBHA8ep6sxAK7LbgDuCRodvBCpV9V+N3D8St8XXr4ArVLU2UBZxnqre1MjxY4BlqrpWRBzg/4ApqjqlwXGxQHrD12W8ZUmuMd7zATeo33+nOE5M0YT32XjPPdQV7H2T++7ff4fEJ/DaX2a0yNXNgl32yKHUVJTw+GXneh2KCWEXP/I0KdmtyXt8DtUri/fvzQTihmSTcnxndWIjBLcn7PW4ra/MXhKR+MbKERo5TnaWSBtTz8oVjPFeHXCfOE5vYGLySSfS5ZNPNOWM8bAXS8FmXHctvsREFrTQ5XuDpbaOIzLKd0C3DjO7Z9Kzj7udFg7P2a/3iWyXQNZVA0k7vTsS49uEOyp4KZbg7rPdSXADx1mCa3aLJbnGhI5c3Aka452E+A2t77yTDv95leju3Xf/CkHL937fQpfvDdZ3jNvP1ZJcsysr5/xA8aY8YnqkEZWT2OTXd+IjST2tG9lXDyKyXUId8KCIdAMmNPnNjDFNwpJcY0KLAm+J4/QEHokdONDf6d13yLrxRiQ2dpcnt/5ry1++N1j73mlAePfHNU1n8vNPoaokNuVorgMJo9rQ6g9DNX5YK3AnTQ0AbsBtOWWMCVGW5BoTmoqB60TkIBxnVvolF9Pl44804bCxOzzBSU0l6YSWvXxvQ4kZsRSsW0PJ5k27Ptgc8JbNmkFpwWZie6UT2Xa7Ban2WFSnZLKvHawpJ3ZBon1rgfG4s/Dn7/PFjTH7nSW5xoS2WSIyHLgmIju7LOfxx2n36KNE5mw/UvXL8r25LXb53mA5vdPw+RxW/jTb61BMCzLlpWcA9qk215cURdrZPcm6vD8R2XE1wN9EpCfucq1WD2pMC2FJrjGhrw74lzhOD+DNxKOOpMvHH2nWzX/ESXb7gobL8r3Beo1sDVipgtkzi7+dRmlBAbF9MohsHb9nJ/uExLHtyL5xqMYNyAT4QER6A38GdmtSlDEmdFiSa0zLsQ53EYmxRET8kH7BBXT9/DNN/c1vaPvgg+7yve+07OV7g7XploLf72f1gpbd59c0v6mvPAtA4mG7P5ob0yOVVjcM0eRjOiGRzjLcBQpOxF0kwRjTAlmSa0zLM1VEDgLOdRIS1ra69Raiu3WjcGM5y+fkex1bk3B8QmxSJBuWLqaq3AbQzJ5Z+PVUygq3ENc/k4jsuJ0e60uPIf383mRc2BdfWkwFcIuI9MVdlMAY04JZkmtMy+QHXhXH6Q68CpCSHcf4m4fSrleqt5E1gR7DW+E4jrUOM3vtq/+8CEDSDkZzfanRpJ7WjVa/H0psr3SA10WkB3A3UNVccRpj9h9Lco1p2SqAc0WkM/BAZvvEmpOuG8RJvxtIVsem7xXaXLoOywasHtfsvflTJ1FeXETsgEwiMn9pv+dLjibl5K60unEY8cNagfA/YAxwNmBLshoTRizJNSY85AK/F5GuwPNte6T6x988jGMu70tm+5aX7GZ3SKSmqor1SxZ6HYppwaa9/hIiQuJhOThJUaSc2IVWfxiqCSNag8M3wBEiMhb4yttIjTH7g9jqeMaEpd7A33GXHGXVggJ++GQFaxcXehnTbolJiOCi+0aTO3sW7959h9fhmBbuqmdeIyY+AfyqEuGIqs4Qkf8DPsfagRkT1iK8DsAYs18sAE4B+gN/zOmVelb73mnOxtwiZn26ktw5m0L213uf0W0REStVMPskKTObg046najYWBVHRIUNwCUiMpGQ/d9vjGlKNpJrzIGhM3Cjql4sIlFbNpTpD5+ulMXfbcRfF1o/A8bfPJSsjkm8+Ier2bRqhdfhmBYmrW0Ow048jd6jD8Px+QiM3L4L3Islt8YcUCzJNebA0gq4TlV/KyKJpQWV+uOkVbJg2jpqq/1exwbAZY8cSk1FCY9ffh7YzyezO0ToOGAwQ447iY4DBtfvnYxbsvMlltwac0CyJNeYA1MycKWq3iAimZVlNTrni9Uyd+oaqspqPQsqtVUc59wxgp+nTeHjR//pWRymZYiIjqbPmMMZfNxJmtamnahqnYi8DTwETPc4PGOMxyzJNebAFgtcoKo3iUjH2po6XTIzT+ZNXUPeipJmD2b0md3of1gOnzz+EPOnTGr2+5uWITE9k4G/Op4BRx6r0fHxon5/kTjOk8BjwCqv4zPGhAabeGbMga0CeFxEngbO8EU4V/ca2Xpkr5GtyVtVwrypa1jy/cZmK2Vo3zsNgFVz5zTL/UzL0q5XXwYefRzdhh9cX2+7GHhIHOdlwJbGM8Zsw0ZyjTENDcAtZThPROKqKmp10bfrZcG09WxeW7pfb3zFY2Mp2rie56+/fL/ex7QcCanp9Bl7BH3HHqUprVpLYPenuCUJn+Gu/meMMduxkVxjTENzgCtE5CbgvKgY35X9D8vp0/+wHPJWFrNg2jqWfL+R6sq6Jr1pu56p+HwOK3/6sUmva1oexxdBlyEH0ffwo+k4YDCO46B+fz7wIvAcYKuEGGN2yUZyjTG7IsBw4BJVPVtE4mqr63TpD3mycPoG1i3e0iRNEI6+uA/dhmUz4b6/sXTmt/t+QdPipOd0oN9hR9F7zOEam5hUP5HsI9zE9mOgxuMQjTEtiCW5xpg9kQCcoaqXiMhIgPKSal3+Q54s/SGfdUsKUf/e/Uy54O6DiU2K5N8Xn01VuZVXHihSslvTfeQhdB9xCNmdugCgqktE5BngZWC9pwEaY1osK1cwxuyJUuA5EXkO6AWcHZsQOb7voe169j20HRUl1brsx3xZ9kMeaxfvfsLr+ITYpEg2LFtsCe4BIDm7FT1GHEL3kaN/SWz9/iLgbdz/X99gvW2NMfvIRnKNMU2hDzBeVceLSG+AitJqXf5jviz9IY+1i3ae8PYc2Zojzu/F9Ldf55s3XmmumE0z2prYjjiE7M5dAVD1F4s47wBvApOAai9jNMaEFxvJNcY0hfnAfBG5A+gNjI+JjxzfZ3TbPn1Gt6WytEaXzXZHeNctLqSudtsJ8d0OygZg1dzZzRy22V/EcWjTvRedBg6h08AhZP0yYlsMvAu8IeJYYmuM2W9sJNcYsz/1Ak4PjPD2A6itrtO1i7fIqgUFrF5QwJYN5VzywGgcn5/HLjqTulrvVlwz+yY+NY1OA4bQcdAQOvYfpNFx8QKgfn+BOM6HwBvA51hia4xpBpbkGmOaSw/gZOBXqnqIiEQClBRUakJqtOSvzOWtv/+ZiuIiT4M0u88XGUnrrj3oWD9a27EzAOr+YvlORCYCE4FZQNP2nDPGmF2wJNcY44UEYCxuwnuyiLSrfyJ/1QpWzZvD6vk/sW7xQkt6Q0h0XDxtuveibc/etO3Zh1Zdu2tEZKQ7Wqv+zSJOfVL7GbDJ02CNMQc8S3KNMaFgDDAQGK7qP0LEya5/onDjBl2/ZKGsX7KQdYsXkr9yBf46K2loDvGpabTr2Ye2PfvQtmdvMtt3RBwHAFV/qYjzNfAVblI7C1t9zBgTQizJNcaEGgF6AocCI1R1hIj0qH+ytrpaNy5fIuuXLGLdkkVsXL6U4k15NMmKFAewxIxMsjt2IatTZ7I6diGrY2dNzMisX0YXVX+eiPM/3KT2K+AnrATBGBPCLMk1xrQEacBBwEhghKp/hIiTVP9kTWWlblq9UjatXsmm1SvZHPhYVrjFs4BDlS8ykpRWbcjq0ImsTl3I6tiZrE5dNCY+ISih1VpggYjM4pekdhnWu9YY04JYkmtMGBORDGCzht83uoM7kW0EMADoq35/P3GcrOCDKkqKddOqlbJ5zUoK8zZSlLeB4vw8ivI2UFUWvotOOL4IkrOySW3dhpRWbUht3YbUVm1Ibd1WE9MzpL7kAEBVy0VkNvBjYJuN2xKu0ovYjTGmqViSa0JeYBZ+X1X9MWhfHJAMVLH7dYAOEAOsb5j0ichoYKGq5gftSwJaq+qi3YgxEXgKuFtV5wTtvxoYClyuqlU7OT8pEN/uqFHVXWZoIpIFrADOVtUJu3ntli4Td2GKvvWbqr+/iJPY8MCqinIt2rhBivM3UpSfR1HeRko251NRXExFcREVJcVUlJaEXBmELyKCuOQU4lJSSUhJJSEtPbBlkJiWTnJ2a03KzBLH2fa/k6qWA0tEZAmwBJiDm9QuxWppjTFhyJJc4ykR6Qu0B2KBOCAVyAhsbYHOQHcgCviNqr4cOO/XwPO4SW59XWA0EIm79CxAElAO1M9ScgL3yVTVwgZxLAIWqOopQfsuAp4GBqrq3F28jnOAV3HrSL/FTURVRJ4BhqnqgMBxEngtNarqDzp/KdBlF1+uep+q6jG7c6CIfAAkqurY3bx2OBKgDdAJ6Bj8UVU7ATki4mvsRL/fT2VpiZYXFUpFcTEVJcWUFxdRXV5GbU01tdXV1FRVUVtdRW11NbVVVdTUuB9ra6pBFXEcRBzEEfejiPt5YL/j8xEVG0dUTCxRcXFEx8YSGRNHdFzsNvtjEhJJSEnT6Ph4aSxWAFV/GcjyoER2aeDjEmA9Vm5gjDmA2Ipnxmv9gVuAzYGtEDcJGQLcivuLeUPQBoCqvoqbVG4lIn8CjqxP6ERkA3CWqk7ZWQCBRLs7cFHQPge4AXeEazyw0yQXOBuYjpuUTw1cI/geDZOLfsC8oMc1wDWq+q+gczYBN6rqC0H7XsD9Q2B3PQgMEBEJw5KF3aXA2sA2LfiJwL9RBO4fVJ2Adrh/YGUCmY7jZMYlJWfGJiZlopopjrMnX/smoeovRylBpEBENgIbcb8XNuJ+f6wF1gBrRZzi5o7PGGNClSW5xjMi0hN31HNcg6d+jbtS1kcN9mcFShfKVXWtiCQAJY1cNziZmxyUbK5U1Y6NhHI18IOqfh2071zcEeYjgAki8pWqfr6D19EXOB64FHgbt51S/Qjz60AZcHHg8AjcHrHrGlxmT3piNTqjXUTewE3IG/NAcNIdkKOqa/bgvuGqFlgZ2BolIvBLQpwOJOK+K1D/DkRsI49jAqf7cf/N/I1sdYGtBCgObCVBH0tFnFp2OHZrjDFmRyzJNV6ajvt/sGHSFoWbIMxu5JxI4L+4o671Na7jcEfobsRdYOCEwP5FuMnlNOBC4MqGFxOR1sB5wP1B+zoADwB/VNX/Bepq3xWRU3aQ6P4Z9y3xr1W1hKDEO5CIr2hQHtFYk/w9qYnc0bE1wHu4I+M7cwpwF798/czuq8UdQd3odSDGGGN2zpJc4xlVTYWtiWDw/8ULcRPWPg1OKVfVrWveq2pNYNS2VFULRaQSqK1PKBs8V4GbBDb0N9yRN3/gnHbAp8Bnqvp44D6vBvZPFJG7gH+oakXg+LHAGcEXFJErgX8B9f2reorI6bgJeqWqZrM9BR4VkUcb7H9eRJ5vsO/tRs4n8PqKVHXhDp6vj2994FPrcWqMMSZs7e5sbmP2p/dwE8L67QHciUJbGmznB58kIhGwR2/kRjY4/3DgAmB14PEY3PKJ3MD+rVT1HuC3wM3ATBGJDHRUeAa3f2iwKiBXVTOCN9wyjB21ZRLcmlyp33BrlC9ssO/Fnby+PU1abUa9McaYsGUjuSYUVAKPALcFHl8M3ITbB7XefLZ/e72+5jG47nanNblBx8ThTlx7j19mnPcCvgD+CvwgIieq6vLA8Wtwk9xhQEZgFLkPbgeHM4DJQff0A10amWy2TQwNNMUfnD4gOVDrvDOtAx+t0tMYY0zYsiTXhII64NrAFqzhpLLglltxQEVgdLN+XyzwFu6krssa6YUrIhIPoKplInIxbp/QRwP7ngSeFJHOuKUSRUGnp+C23NvaA1dVZwcSysYS1GWq2rXB/U/ALWNoTAQQKyIpwacAcQ32Re3gfHBXBRsHnLyTYxre0xhjjAlLVq5gQoEPeBLICWy34LZIygnaitn2/+sCoFZEtH7D7Yl7HHAJ4A9+LvC8H7eH7j0Aqvqxqq5ne9G49a0FQftqaaQDgqoWNNwX0EFEVgRvwLM7+RrEAPeybXlGGvBYg31n7+QamcBzweUNjW3Awbgj1gdqSzFjjDEHAEtyTSiIAS7HrY1dDfwDaBX0eDVuWUBM0DmHBI5JDWy34Sawo4P2nYZb4jA08Dgdtw/q7buIJxHI38e+sitVtWPwxi9txBqTAZyztzW5gcUM+uIm/zulqt+o6pGq2liXB2OMMSYs2NuVxlMiEo3bY3brCmAicgXwJ1VtF3RcBBAlIhGqWhvc31VEzsPtkvAD0BM3Ka7DLUO4XVVn7WFYXdi+j+2e2u2aXBFpi9s79+d9uN/YwDWm7eI4Y4wx5oBgI7nGa7fjrnJWKCKFIlJIoLtC/ePAvkLcGtmTGrnGx7i1qJ/gvp2/AFiI203hq8DqZXtiHI336N2V4IlcyxopE2i46EW9w3Bf2097cc96v8dNkr/bh2sYY4wxYcNGco1nRKQN7mjrfWxbH7qjPrkO7uSstOBaWFXdLCJf4JYmtAOGA0/hdhH4BKgSkYnAm8Cnwb12A3xBMR2C2y1h7B68lPrJYKmB1c+ygUgR6drguNZAhIj0CMS9KLD/fOD9+pHsBq+3YYKeRIMuEyIyHjgWuOgAXrrXGGOM2YYlucZLE4FuuC3EGuvZurTBY8FNKJ8WkX8CL+G+RZ8CdMadrPYa0FtVV8HWcohjcGt+3wOmi8hhqhq8MEQ04BOR4cA7wH9VteHb/pHs+PslOvCxH/AwUB14PTN3cPxMYBYwVkRGAUcChzdyXFRgQ0QG4LYpSyWoC4WIxAB/AWYEvh7GGGOMwZJc4yFVHbAv54vIh7hJ3xLcpHFhw5FMVa0CJgATRKQTbu1vw5XPInAntWXglgxc1sjtotl24lvwPXL5pVThyT18GSXAfao6uZHnjuOXhSbm4vYSnqKqU4LuXSkiQ4B0VbUVzIzZj0QkA9hs75gY0zKIfa8aY8yBQ0Qigb6q+mPQvjggGbcUZndXwnNw//Bb30hP6gdwe1ZPD1x3R0pVdbvWfCJyPTBTVb9qsD8at63eQ6o6b0cXFZEkdn/OSY2qlu3qIBHJAlYAZ6vqhN28tjHGQ5bkGmNMmAjUhLcHYoE43Hc6MgJbW9yynu64ZTC/UdWXA+f9GngeN8mtf0cgGrdMpzTwOAm3F3V9UuoE7pOpqoVBMWQAG4GzgA8D5+zIcao6sZHXsRq4X1UfarA/Ercc6LDgdzQaOX8pbpeU3fGpqh6zOweKyAdAoqqO3c1rG2M8ZOUKxhgTPvrjLqayObAVAp2AIcCtwHrc2vX6DQBVfRV3meutRORPwJH1CZ2IbADO2llyGXA2sAp4m1/KePo1HHkVkU1sv1R3vSqCVhwMrFRYyS8Jdl1gf3Qg/obXqQGuUdV/BV1jE3Cjqr4QtO8F3D8EdteDwAAREStZMCb0WZJrjDFhILDE9Lds36ru10Av4KMG+7MCI6PlqrpWRBLYfiltGvR7niyytVPeysAiJw1dBjyvqn4JOngHgpfqjsAdOa5PWDUQXxxush7sf0GXvh+3G0uw7UogdqLRWnYReQMYv4NzHmjkpeUE9+82xnjPklxjjAkP03F/pjdM2qJwa2dnN3JOJPBf4CJ+SS7H4S4qciNuK70TAvsX4a7aNw23zd+VDS8mIqfirrx3f2BXfV1sooikNDycbetmxwKfBz1+PrAdhZvoVgUSZwVGq+q0QHeRxkZUd7eueGfH1uB2ZLllF+efAtzFjkeljTEesSTXGGPCgKqmAgRGZIN/tu+o73R5cM9oVa0JJJClqlooIpVAbX29bYPnKnCTwK0CI7F/a3CP+o4k3+wg7OCOJdNw+1xX4S5qcj/wLlBBIMFt5DVXiogjItENShYUeFREHm1wyvMi8nyDfW/vILYaoEhVF+7geQBEZH3gU+tuYkyIsRXPjDEmvLwHbAnaHgDaNNi3BXcRkq0CSequyguCRTZ4/H+4PauX1O9Q1bIGq/6BOwpbv+/joGMrVXWtqm7CHV0tU9V1uKO7dSKiQaUTXwU9rsNtE7jNy8GtyQ2+92bgwgb7XtzJ69vTpHVPRo+NMc3ARnKNMSa8VOL2VL4t8Phi4CagR9Ax89n+7fX6UdXgutud1uQGHZMF3AycWX/fwKSw2p31bxYRHxClqhWNPD1WRC7C7dJQwPZJ5Bbclf5+YPuShaYYwPEByYFa551pHfi4J38gGGOagSW5xhgTXupwV8W7tsH+hpPKgid9xQEVQaOtiEgsbq/bdcBljfTClUDXA1Q1T0RGq+oMEalPrh8GLm9kgtZXDfatBDoGrpkDnIubOJ6Au+T3BtxyhYblEeC2N9tM0NLcARG4S4CnBJ8CxDXYF8WOpeHWJ5+8k2Ma3tMYE0KsXMEYY8KLD3flvZzAdgtuopgTtBWz7c//BUBtfQlAYPS2HHfVvUsAf/Bzgef9uEnmPQCqOqNBHH8O3KsN8ClunS24PXTvDOzPAQ4FEJHzcRPeq3AT9RtV9R7gIaC6kfsDfIXbSeHWBveOAe5l2/KMNNyFJIL3nb2Tr2Mm8FxweUNjG3Aw8AWNT4AzxnjIklxjjAkvMcDlwOrA9g+gVdDj1bgLOwRP+jokcExqYLsNN4EdHbTvNNwSh6GBx+m4E8VubywIVc0PtNQaCByOm7wSiOdqIFVV16hqfdnDh7h1vV2BvKBL3Yy7gEW3oFjqe9sei5ssP9Tg9hnAOXtbkxsoo+iLm/zvlKp+o6pHBmqJjTEhxN5eMcaYMBGogz0ed6laf2DfFcCfVLVd0HERQJSIRKhqbXB/VxE5D7dLwg9AT9ykuA54FLhdVWftQTw9gVeA21R1VqDEYBbwOPCZiIxX1ekAqro5cF+CyxlUdZOITABWqerZQdcGt9vDeoKISFsgAfh5d+NsxNjANabtwzWMMR6zkVxjjAkft+MunFAoIoUiUkigu0L948C+QtwVxU5q5Bof49aifoL7dv4CYCFuN4WvRGRXvzcEt2T3aNxygrdU9b4Gx/wfMBF3UYcXRSRxF9d8BThLRA7exXEAh+G+tp9249gd+T1ukvzdrg40xoQuS3KNMSYMiEgb3NHWNkB73MlcHXFLD9YHPe6IW2aQDXwrImnB1wmMqH4BTMEdxVXgqcC+T4CNIvKSiIwTkcYmbjlANG4C/TKBRSOCkmNRVb+qXopbVrFWVRtOimv4u+kZ3MUoejS4VmPOB95vpK+u08h1k9i+3+943DKI+2zpXmNaNitXMMaY8DARt261ksZ7ti5t8Fhwuws8LSL/BF7CfYs+BeiMO1ntNaC3qq6CreUQx+Amp+8B00XksAadD2IAn6r+tsH9ooOeB0BVn9smIJEzcUsF2hNUlxtYqGKYqpaIyIXAgMBTWxqcPwo4ErcGuKGowIaIDAAm49b2Xht0fgzwF2BG4OthjGnBLMk1xpgwoKoDdn3UjonIh7hJ3xLcutmFDUcyA6uKTQAmiEgn3NrfmgbH9NpBfBXsupdsBO5kt/txOzIEn18/2tsLOB23U0LDutsS3BHYyY1c+zhgWeDzubi9hKeo6pSge1SKyBAgfWf9fY0xLYPYuzHGGGOMMSbcWE2uMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiwY0muMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiwY0muMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiwY0muMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiwY0muMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiwY0muMcYYY4wJO5bkGmOMMcaYsGNJrjHGGGOMCTuW5BpjjDHGmLBjSa4xxhhjjAk7luQaY4wxxpiw8/9Ys9ejGa2yegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6), dpi=120)\n",
    "temp_df2.plot(\n",
    "    kind='pie',\n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.8,\n",
    "    wedgeprops={\n",
    "        'width': 0.3,\n",
    "        'linewidth': 1,\n",
    "        'edgecolor': 'white'\n",
    "    }\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78e12644",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
